An Investigation of Qualitative Research …990443/...focus varies between describing a sensation objectively and evaluating the audio subjectively, depending on the research focus

An Investigation of Qualitative Research Methodology for Perceptual Audio Evaluation (Licentiate thesis) By Dan Nyberg Copyright© Dan Nyberg (Thesis), Acta Acustica united with Acustica and Audio Engineering Society (included papers) 2012!

!"#$%&'()## # Licentiate thesis# # ##

!"#!$ !$

Abstract This thesis investigates whether a qualitative research method, using phenomenological interviews and analysis, can be successfully applied to perceptual audio research, a field heretofore that has mainly used quantitative methods. The method is investigated by studying the types of information elicited by the method and the information’s usefulness and relevance to the conducted study. The qualitative method is applied in three different conditions: a non-experimental condition, an experimental condition, and an experimental condition using mixed-methods. The thesis also identifies implications associated with using a qualitative method in a quantitative field of research, implications that researchers should acknowledge and consider. All scientific criteria in which the quantitative research is judged cannot directly be applied to a qualitative method. A qualitative method has to be judged on its own framework, departure points, and scientific criteria. The information elicited from the qualitative method contains information that supports known knowledge and adds new knowledge. It supplements the accessibility to the subjects’ perceptions and used methods when conducting a perceptual evaluation task. In conclusion, a qualitative research method that consists of phenomenological interviews and analyses can be successfully applied in all the tested conditions.


!"#!$ %$

Preface This licentiate thesis contains work performed at Luleå University of Technology, the institution of Art, Communication and Education in Piteå, Sweden, during 2009-2012. The thesis include research from the Vir Music Project financed by the European Union program Interreg IV A Nord, research in cooperation with the Swedish Radio, as well as individual research conducted by the author. This thesis would not have been accomplished without valuable comments, discussions with supervisors, colleagues and the project and co-operation members. So I would like to take the time to acknowledge these persons. Special thanks to my supervisor Associated professor Jan Berg for letting me have the opportunity to develop my intellectual capacity and making this thesis is a reality and for having the openness to inspire the use of a methodology new to the research field. Many thanks to my co-supervisor Dr Anders Persson, for the rich and valuable comments and discussions on the work, including large and small issues. Many thanks to all colleagues and members involved in the project and co-operation in which enabling the research. From the Vir Music project the author would like to especially thank Morten Amundsen and Noa Nakai for a successful collaboration on making the Master Classes happen both logistically and with equipment. Thanks to Sture Brändström, Erik Lundström, Outi Nissi, Jenny Kilponen and Marrit Rajamäki for planning the master classes and helping with the design and distribution of questionnaire. Thank you to Lars Mossberg, Christofer Bustad and Lars Jonsson of the Swedish Radio for letting my research be a part of your research. Special thanks to Lars Mossberg and Christofer Budstad for a successful co-operation with the listening test in Piteå and for conducting the interviews in Stockholm. The author would also like to thank the PhD student colleagues, Jonas Ekeroot and Oscar Gedda, for valuable discussions and comments on ontological and epistemological considerations. A special thank to Jonas Ekeroot for having the time to give comments on the articles during the writing process. Thanks to Lars Hallberg and Christer Wiklund for giving the financial support. Thanks to all participants in all studies, without your participation this work would not have been possible. Hopefully the work and the results from this thesis will be “a thing that wouldn’t leave”.


!"#!$ &$

*+&,'#-.#/-01'01# Abstract .............................................. 2 Preface ................................................ 3 1. Introduction ................................... 5 2. Theory and background ............... 6 2.1. Definition of quality .................................6 2.2. Point of departure .....................................8 2.3. Distinctions between a qualitative point of

departure and a quantitative point of departure ...................................................10

2.4. Perceptual audio research .........................10 2.4.1. Physical domain .....................................12 2.4.2. Sensory domain .....................................12 2.4.3. Affective domain ...................................13 2.4.4. Response format ....................................13 2.4.5. Implications of perceptual audio

research ................................................13 2.5. Mixed methods research ...........................14 2.5.1 Implications of mixed methods research 15 2.6. Qualitative methods ..................................15 2.6.1. Phenomenology .....................................16 2.6.2. Hermeneutics .........................................16 2.6.3. Grounded theory ....................................17 2.6.4. Comparison of qualitative research ............................................................17 2.7. Summary of theory and background ......................................................18 3. Research question and focus ........ 18 3.1. Motivation .................................................18 3.2. Aim ...........................................................19 3.3. Objectives .................................................19

4. Summary of papers ..................................20 4.1. Paper 1 ................................................................... 20 4.2. Paper 2 ................................................................... 21 4.3. Paper 3 ................................................................... 22 5. Summary of results ..................................23 5.1. Types of elicited information ................................ 23 5.2. Relevant information for the study ....................... 24 5.3. Usefulness of the information ............................... 24 6. Discussion .................................................25 6.1. Relation to quality aspects .................................... 25 6.2. Framework of Validity and reliability .................. 25 6.3. Interviews and analyses ........................................ 26 6.4. Information elicited from the studies .................... 26 6.5. Using the qualitative methods ............................... 27 6.6. Original contribution ............................................. 28 6.7. Further work .......................................................... 28 References .....................................................28 Appended papers: Paper 1: A qualitative approach to evaluation of perceived qualities of audio and video in a distance education context Paper 2: Perceived audio quality of realistic FM and DAB+ radio broadcasting systems Paper 3: A Descriptive Method for Unravelling the Composition of the Black Box of Affective Judgment in Sound Quality Evaluation


!"#!$ '$

1. Introduction Perceptual audio evaluation requires research, prediction, evaluation, and generalization of perceived sound quality. These procedures frequently involve using models and numeric data collection. This way of quantitatively evaluating perceived audio quality has been highly effective and the main mode of investigation; such conditions allow the researcher to control for all parameters during an experiment and involve rigorous control over the parameters in the experiment. This strategy is done to collect answers that are related to the factor under investigation and to control and estimate any bias. In addition, this strategy often does not allow test subjects to add information about their own perception of the sound quality. This type of investigation has several implications. First, as perceived sound quality can be highly subjective, controlling what factors the subject evaluates eliminates the ability of the subject to add information about a perception. This limitation can mean that the researcher will miss important information related to the evaluation. Second, controlling all parameters might not be possible in all situations. This is especially true in real-life situations where evaluation of sound quality is needed, i.e., where interaction (e.g., talking or collaboration) between subjects cannot be interrupted. To deal with these implications, a new data collecting method and analysis that can add more detailed information and a different aspect on a perception might be valuable. This approach shifts the focus to qualitative methods frequently used in the social sciences, humanities, and arts. These methods focus on the individual subject’s perception and experience. By allowing the subjects to describe and reflect on their perception in their own words (during, after, or even before), a sound quality investigation could be even more useful. The approach of letting subjects use their own words to describe their perception, in the form of attributes, is not new to this field; it has been successfully applied in the field of perceptual audio evaluation in a more quantitative manner [1]. However, this shift to entirely qualitative methods comes with implications. For example, the researcher has to be aware how qualitative methodology differs from perceptual audio evaluation methods. This study investigates whether a qualitative method, phenomenological interviews and and analyses (techniques often used in the humanities, social sciences, and arts), can generate useful and relevant information to the field of perceptual audio evaluation. In this study, the terms audio quality and sound quality are used interchangeably. The thesis begins with a background and theory section that presents definitions of perceived quality, the ontological and epistemological departure points, a discussion on how qualitative and quantitative research differ, other points of departure, and the research focus. The thesis contains three publications/studies, all of which use a qualitative method. The method is applied in three different conditions to achieve the thesis aim:

• a non-experimental condition; • an experimental condition; and • an experimental condition using mix-methods.


!"#!$ ($

2. Theory and background This section defines the point of departure and what is meant by quality. The foundation in which the research rests on is also presented, incorporating previous research and methodological considerations.

2.1 Definition of quality To study perceived audio quality, a clarification of the term quality is needed. Martens and Martens present four definitions of quality [2], seen below: • Definition 1: Quality as qualitas.

Underlying assumption: People describe the world. The focus is on the inherent properties of an entity (e.g., a system) so as to describe the properties of an entity objectively.

• Definition 2: EXELLENCE/GOODNESS:

Underlying assumption: People evaluate the world in a cultural context. This definition considers quality as the degree of excellence. This definition is in conflict with definition 1. And seen from a market study point of view, there is a danger of defining a degree of excellence from a single individual. In a market study approach, inter-subjective patterns (i.e., patterns of responses common for a whole group of subjects) are sought. Thus a statistical representative quantity of a specified target group is needed.

Definition 3: STANDARDS: Underlying assumptions: People relate the descriptions of an entity to an evaluation of good and bad, making quality a relational concept. Quality is thus a combination, to a certain extent, of both definitions 1 and 2. The focus is on the description of an entity and the stated/implied requirements.

• Definition 4: AN EVENT:

Underlying assumption: People implicitly experience quality as an event that can only be lived. The focus is shifted from a third person point of view to a first person view. The quality is seen as a subjectively experienced event, combining knowledge about “what is” with feelings about “what is best”. The form of quality approach can be considered as the ultimate subjective and phenomenological view.

The first three definitions of quality are often seen in perceptual audio research. The focus varies between describing a sensation objectively and evaluating the audio subjectively, depending on the research focus. For example, when defining basic audio quality [3], an assessment of the overall audio quality is sought; in Lorho’s definition [4] of quality, a specific character of quality is under investigation (e.g., spatial impression). As Jekosh [5] points out, it is also important that there is a distinction made between quality seen from a producer´s perspective and quality seen from the user’s perspective: the former relates to product sound quality and the latter relates to perceived sound quality. Table 1 shows several commonly used definitions of sound quality.


!"#!$ )$

Sound quality can also be categorized into four conceptual layers based on the amount of abstraction involved in the methods based on the properties of their source perceptions (Table 2). Auditive Quality uses the lowest level of abstraction, and Aural Communication Quality uses the highest level of abstraction [6]. This research relates quality aspects to perceived sound quality and not the product sound quality. Furthermore, the concept used in this thesis coincides (largely) with definition 3: Quality is a rational concept between evaluation of good and bad and the description of an entity. However, few perceptual audio studies focus on the fourth definition of quality. This inconsistency raises an interesting question: What information can be elicited when investigating perceptual audio quality with a focus on the subjective? Table 1: Examples of definitions of quality in perceptual audio research.

Examples of definitions of quality in perceptual audio research ITU-Recommendations BS. 1116 [3], definition of Basic Audio Quality (BAQ)

“This single, global attribute is used to judge any and all detected differences between the reference and the object”.

Lorho’s [4] definition of quality

“A measure of the distance between the character of an entity under study and the character of a target associated with this entity”.

Jekosh [5] definitions of product and perceived sound quality

Product-sound quality: “Product-sound quality is a descriptor of the adequacy of the sound attached to a product. It results from judgments upon the totality of auditory characteristics of the said sound- the judgments being performed with reference to the set of those desired features of the product which are apparent to the user in their cognitive, actual and emotional situation”. Sound quality: “Result of an assessment of the perceived auditory nature of a sound with respect to its desired nature”.


!"#!$ *$

Table 2: Synopsis of conceptual layers of sound quality (Taken from Blauert and Jekosh [6]).

Conceptual Aspects Examples of Issues Suitable Measuring Methods Auditive Quality (Classical Psychoacoustics)

Perceptual properties such as loudness, roughness, sharpness, pitch, timbre, and spaciousness

Indirect scaling: thresholds, difference limens, points of subjective equality Direct scaling: Category scaling, ratio scaling, direct magnitude estimation

Aural-scene Quality (Perceptual Psychology)

Identification and localization of sounds in a mixture, speech intelligibility, audio perspective incl. distance cues, scenic arrangement, tonal balance, and aural transparency.

Discretic: semantic differential, multi-dimensional scaling. Syncretic: scaling of preference, suitability and/or appropriateness, benchmarking against target sounds

Acoustic Quality (Physics) Sound-pressure level, impulse response, transmissions function, reverberation time, sound-source position, lateral-energy fraction, and inter-aural cross correlation

Instrumental measurements with physical equipment for measurement of elasto-dynamic vibrations and waves, including appropriate signal processing

Aural-communication Quality (Communication Sciences)

Product-sound quality, Comprehensibility, usability, content quality, immersion, assignment of meaning, and dialogue quality

Psychological (cognitive) tests, particularly in realistic use cases, e.g., the product in use, the audience in concert, etc., questionnaires, dialogue tests, comprehension test, usability tests, and market surveys

2.2 Point of departure This section presents some of the crucial ontological and epistemological underpinnings that inform this research. According to Hansson [7], science provides us with common parts of our worldviews. This knowledge should be inter-subjective (common to all humans) and aim to obtain knowledge of how things really are. Thus, science should strive to produce objective knowledge. The demands of objectivity and inter-subjectivity can be stated in three assumptions on which science is based ([7] p. 11):

1. There is a real world that is independent of our senses. 2. This real world is common to all of us. 3. With combined forces we can achieve, or at least approach, knowledge about this real world that

is common to us all. Hansson [7] states that we have no direct access to physical reality (we only have access to the impressions our senses perceive). There is no simple or direct way to check that a statement is objective although there is a way to check whether a statement is inter-subjective, whether anyone could arrive at the same conclusions using critical testing.


!"#!$ +$

This understanding, according to Hansson, makes inter-subjectivity a requirement for scientific knowledge. One important distinction between objectivity and inter-subjectivity is that inter-subjectivity can be reached without objectivity since people can share prejudices. Objectivity, on the other hand, includes inter-subjectivity, since the demands for objectivity are the same for all people. Thus we try to describe a single reality as accurately as possible [7]. Patton [8] argues that all our understanding comes from our sensory experience of a phenomenon and this experience should be described, explicated, and interpreted. However, this approach is difficult since descriptions of experience and interpretation are highly intertwined. Hence, according to Patton, there is no separate or objective reality for people: there is only what people know their experience is and means, so the subjective experience incorporates the objective thing and people’s reality [8]. For Hansson, a researcher should always strive towards objectivity even if it is not possible to achieve fully [7]. This is essential both for the production of knowledge (facts) and for knowledge that results in action:

When a historian makes an account of how World War II started, or a chemist explains a particular chemical reaction, we expect their statements to refer to and describe reality; they should not report personal suggestions or ideas. ([7] p.11)

This view on knowledge is the foundation of this work, where the subjects’ perspectives are the focus. It also illustrates the difficulties with perceptual audio evaluation. Perceptions of audio quality are interpretations made by the listener, but the distinction of what is perceived (objectively) and how the listener interprets this perception are intertwined from the researcher’s perspective. Because trained listeners can consistently discriminate differences/similarities, this problem can be minimized. However, trained listeners can be too discriminating in their evaluation when looking at for an example of the overall quality. In these cases, untrained and naïve listeners might be more suitable because they do not analyse the sound to the same degree as trained listeners [4, 9]. Blauert and Jekosch [6], using another point of departure, present a new way to categorize perceptual audio research methods, perceptionisim. Perceptionisim starts with the assumption that the world is constructed in our brain and is composed of feelings (hunger, pain, etc.), things (sensory events and perceptions), and concepts (ideas, notions, and thoughts). This categorization is based on the amount of abstraction involved in the subjects’ perceptions when using different methods. This approach provides a more clear view on what methods generate what type of data and with how much abstraction. For example, perceptual properties such as pitch and loudness require very little abstraction by the subject, whereas product-sound quality and content quality requires a very large amount of abstraction by the subject [6]. This example illustrates further the notion that perceptions, which are objective and which are interpreted, are difficult to distinguish. However, depending on the method and what listeners use, one can collect data that are more/less objective. When changing the point of departure, one needs to know how these premises are different, in this case, between qualitative and quantitative points of departure.


!"#!$ #"$

2.3. Distinctions between a qualitative point of departure and a quantitative point of departure In qualitative research, the validity of the research does not depend on the replicated outcome, rather the focus here is on the process in which the results were derived from: the way the results were obtained (the analysis) and the way the results represent the collected data. Two researchers will never produce the same results even if they are presented with exactly the same task because of their different philosophical stances and individual styles, creating different viewpoints of the examined phenomenon (research question) [10]. This is very different from quantitative research where the objectivity and the accuracy of the measurement results and replication of results are crucial. In qualitative and quantitative research, inter-subjectivity should be sought (see section 2.2), but there are differences between quantitative and qualitative research. This issue is highly debated and this discussion should be seen as an overview of the main differences. The critical testing of inter-subjectivity on the results in social science are composed of two conditions according to Bergström [11 p. 113]:

1. In principle it is possible for anyone with suitable intellectual capacity and technical equipment to critically test the results; and

2. If several persons should critically test the results, all should be able to draw the same conclusions.

Bergström [11] makes the distinction that all social science results are able to fulfil condition 1 in practice although it is very difficult to achieve condition 2 in practice, but in principle it could be achieved. To fulfil condition 2, all the conditions in which the research was conducted need to be replicated; this can be done in theory, but it is very difficult in practice, especially in situation-based research, which is often seen in the social sciences. Therefore, it is very important for the social science results to fulfil condition 1, but it should also strive to fulfil condition 2 in principle [11]. It is important to know this distinction when comparing qualitative research with quantitative research. In more experimental-oriented research, as with perceived sound quality evaluation, both conditions 1 and 2 are more easily achieved because the researcher can control all parameters, an experimental approach not possible in the social sciences. In this section, the major differences between qualitative and quantitative research were presented. In the following sections, a more detailed description of the research field in questions is presented.

2.4 Perceptual audio research A significant part of the audio research methods has its foundations in the field of sensory evaluation research [12,13] and can be found, for example, in the natural sciences and psychophysics. All sensory evaluation methods have their own guidelines and techniques for response tasks, statistical methods, and interpretation of the results. The general approach of sensory evaluation is to evoke, measure, analyse, and interpret responses collected from subjects’ sight, smell, touch, taste, and hearing senses [12]. This general


!"#!$ ##$

attitude can be seen in Bech and Zacharov’s [9] criteria on scientific method used for perceptual audio evaluation. The following four criteria have been extracted from their work [9]. Objectivity1: Another experimenter should with the same circumstances, with the same stimuli, with a similar data set, and with the same statistical analysis come to the same conclusions during a new experiment and with new subjects. The quantification of the impression: There is a direct relationship between an auditory sensation and attributes under investigation. Answers (numeric data) collected from scales can be tested statistically. The researcher assumes that there is a direct relationship between the testable statement and the answers the subjects provide (the dependent variable). Communication: Using defined and specific attributes in the experiments clearly presents the communication between scientists, clients, and the public. Scientific generalization: The conclusions can be generalized to a lager mass, based on a small representative population of subjects. There are many research methods employed in perceptual audio evaluation research all with their own niche depending on the aim and purpose of the research. The methods can be categorized in at least two ways: the method’s level of abstraction [6] or the perceptual domain. The latter categorization will be described here and is the most frequently used. See Figure 1 for the different domains. The physical domain deals with measurement of the physical audio signal and the sensory and affective domains deal with the auditory sensation of an audio signal. These three domains and their associated research methods will be presented in the following subsections.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#$,-./$012.3.4.53$52$567184.9.4:$./$8;5/1;:$<1;=410$45$4-1$<1;.=6.;.4:$52$1>?1<.@134A$531$?=<4$52$4-1$4<B/4C5<4-.31//$52$4-1$1>?1<.@134D$,-1$/18530$?=<4$./$4-1$9=;.0.4:$52$4-1$1>?1<.@134D$


!"#!$ #!$

Figure 1. Different domains in perceptual audio research.

2.4.1 Physical domain The physical domain is highly objective in the sense that the researcher can accurately measure the physical nature of an audio signal such as a loudspeaker response, frequency content analysis, and reverberation time [4]. No subjects/listeners are needed in this type of measurement. There is also a branch of research that uses objective models/methods to predict or measure perception and audio quality. For example, Bradley and Soulodre provide an objective measurement of listener envelopment in multichannel surround systems [14] and the ITU-Recommendation BS.1387 provides a method to measure audio quality objectively [15].

2.4.2 Sensory domain According to Lorho [4] the sensory domain can be seen as

[…] the elicitation of objective responses to the properties of a stimulus, as it is perceived by the human sense. ([4] p. 7)

This domain can also be labelled perceptual measurement [9]. It is assumed that an auditory sensation is based on several auditory attributes and that the researcher can identify and elicit these attributes. The subjects’/listeners’ task is to quantify the impression of one or several of the elicited attributes during an investigation [9, 16]. For example, Gabrielsson and Lindstrom [17] and Toole [18] elicited attributes and used the attributes when investigating the perceived sound quality of loudspeakers. There are many methods for eliciting the attributes and for using the attributes. The methods can be divided into two large categories: methods that separate sensation of an auditory impression and the verbalization and methods that assume that there is a direct relationship between the auditory impression and verbal descriptors used by the listener/subject [9]. The former method might draw on spatial impressions [19] or indicate direction of a sound [20]. Martens and Zacharov’s research [21] provide another example of a method in which the listener does not label a sensation, the Multidimensional Scaling (MDS) method. Here the focus of the investigation is on the individual differences/similarities of pairs of stimuli. The methods that fall in the latter category can investigate a specific auditory attribute of loudspeaker sound quality [18], descriptive analysis, or attribute elicitation using a repertory grid technique. Koivuneiemi

E 1DFDA$G1=/B<.3F$?-:/.8=;$?<5?1<4.1/$52$=3$=B0.5$/.F3=;$

H-:/.8=;$05@=.3$

E 1DFDA$I./8<[email protected]=4.3F$41/4/$J591<=;;$0.22<1381/$K$E 1DFDA$I1/8<.?4.91$=3=;:/./$J/?18.L.8=4.53$52$=44.6B41/K$

M13/5<:$05@=.3$

E 1DFDA$N22184.91$5<$-1053.8$41/4/$J?<121<1381/A$;.O.3FP0./;.O.3FK$

N22184.91$05@=.3$


!"#!$ #%$

and Zacharov [22] used audio descriptive analysis and mapping (ADAM) and developed a common language (common attributes) to unravel spatial sound perception. Berg and Rumsey showed that a repertory grid technique is a fruitful method for eliciting individual attributes in spatial audio [1].

2.4.3 Affective domain The affective domain methods measure the subjective and global perception of the auditory sensation [4] to quantify an “overall” and “general” impression of the auditory sensation. The listener is assumed to have an integrative state of mind when creating the overall impression. The listener should also form a single impression taking into account the individual attributes of the sound, the context, the mood, the expectation, the previous experience, and traditions [9]. Three tests are associated with the affective domain [9]: • Preferences tests (paired preference tests and ranking based on preference); • Acceptance tests (report the degree of liking); and • Appropriateness tests (report the degree of liking with the context relationship) [23].

2.4.4 Response format The methods employed in perceptual evaluation research can be categorized into three general sensory tests [12] based on their focus:

• Discrimination tests (the focus on overall differences among products); • Affective or hedonic testing (measuring consumer likes and dislikes); and • Descriptive analysis (specification of attributes). Below are some examples of the methodologies and their accompanying response formats. The discrimination tests can also use an ABX comparison [24] using a nominal scale or an AB comparison using an ordinal scale to decide whether two objects are equal. The AB comparison is used to decide whether a magnitude of a sensation of one object is higher/lower than another object [12,13]. Other examples of scales and methods used here are the ITU Recommendations BS.1116 [3] and MUSHRA [25], both of which use interval scales to show how much an object is greater or less than a non-equivalent object [9]. These methods [3, 25] also show the magnitude of the differences. Affective or hedonic testing, where one tests consumer likes and dislikes using a partition scaling, divides the sensory continuum into equal intervals [9]. The descriptive analysis looks at how a product differs in specific sensory characteristics [12, 13]. Zacharov and Koivuniemi [22], for example, use an audio descriptive analysis and mapping experimental design to investigate the perception of spatial sound reproduction. The response format includes a 10-point scale using a one decimal place interval scale that includes positive and negative end words for each specified attribute. Twelve attributes were used and comprised of both spatial and timbral attributes.


!"#!$ #&$

2.4.5 Implications of perceptual audio research Perceptual audio evaluation methods have several advantages since they aim at generalizing and predicting the results. However, there are several implications with the use of these methods. As they are often conducted in experimental conditions, this is not a major drawback; however, the methods are hard to apply in contexts other than experimental conditions where the researcher does not have control over the parameters. This limitation makes case studies very difficult. In addition, it is even harder to arrive at a more elaborated understanding into how the subjects perceive different attributes, differences, and qualities, since the quantification of the impression does not allow the subjects to describe their impressions in detail. Several questions could be investigated to collect a more complete understanding of the subject’s experience:

What goes into an affective judgment? What does the subject actually perceive more than the global quality, good/bad, liking or disliking? What are the differences the subjects perceive in a BS.1116 or MUSHRA experiment?

It is important to note, as Berg and Rumsey [1] and Kjelden [26] discuss, that there are limitations in using established attribute scales. One limitation is often terms used are not consistently defined and have different meanings for different subjects. If a consensus has been established on the meaning of the terms used (e.g., by expert listeners), then the meaning of the term might not be valid for the rest of the world. Another limitation with using established attributes is the fact that it only gives an answer to the question posed [26]. To overcome these limitations, both Berg and Rumsey [1] and Kjelden [26] propose the use of individual attributes. Similarly, many researchers have used a mixed method approach for collecting both qualitative data as well as quantified data to understand the quantitative results even better.

2.5 Mixed methods research Mixed methods research combines several methods to investigate a topic from several points of views. Strohmeier et al. [27] used a mixed method approach to evaluate multimodal quality perception of audio and video. The combinations of methods were preference mapping, sensory profiling, and a method of constructing a link between the preference mapping results and the sensory results. Jymsiko-Pyykkö et al. [28] used semi-structured interviews (quality evaluation criteria) and a single stimulus method (quality evaluation) to evaluate audio and visual quality. The data analysis in the particular research used a combination of grounded theory analysis (finding patterns and building a theory from the data) [29] and quantitative statistical analysis (Bayesian modelling). And in [30] results form semi-structured interviews and grounded theory analysis were compared with quantitative data in order to investigate the relation between produced and experienced audio-visual quality.


!"#!$ #'$

Schulte-Fortkamp and Fiebig [31] also use a mixed method approach. They conducted a case study on evaluation of noise in a residential area using both interviews and acoustical measurements to investigate people’s attitudes, perceptions, and noise in an residential area. The analysis had a grounded theory approach. The results from the study showed that the grounded theory approach could explore, in detail, what influences the process of perception. It was also purposed that further investigation on the integration of qualitative and quantitative data should be conducted.

2.5.1 Implications of mixed methods research These examples show that a mixed method research approach could be fruitful since one can collect different sets of data that can give information from different viewpoints on a particular topic. The latter work is interesting since this research is conducted “outside” the experimental boundaries, which indicates that a similar approach can be useful in a situation that is not fixed to a test laboratory. It is also interesting to see that two of the mixed methods shown have been used in sociology to collect more detailed information. When trying to understand audio perception, perhaps researchers should borrow from the humanities and social sciences.

2.6 Qualitative methods Both the humanities and social sciences are broad fields of research, including sociology, history, and psychology, just to name a few disciplines. These fields employ a wide variety of qualitative research methods that are highly intertwined with their respective methodologies. The methodologies are important to understand when using qualitative research methods because each method has its own framework that the user must acknowledge. Qualitative methods can be productive for perceptual audio research, because qualitative research focuses on an

[…] attempt to understand the meaning or nature of experience of persons [...] getting out into the field and finding out what people are doing and thinking. ([32] p.11)

Qualitative methods are used to explore areas where little is known or much is known in order to gain novel understanding. It can also collect details about a phenomenon. The details can include feelings, thought processes, and emotions [32]. Anselm and Corbin [32] identify three major components to qualitative research (p. 11-12):

Step 1. Data collection. Data can be collected from various sources: interviews, observations, documents, records, and films. Step 2. Analysis of data. Procedures used to organize and interpret the data. This includes conceptualizing, reducing the data, elaborating categories in terms of their properties and dimensions, and relating through a series of prepositional statements in which all are referred to as coding.


!"#!$ #($

Step 3. Written and verbal reports. The results presented in the form of articles for books, scientific journals, or conferences.

To interpret/analyse the collected qualitative data, two main ontological standpoints and many methodologies of qualitative research come into play, phenomenology and hermeneutics, because of their predominant theoretical impact on issues concerned with understanding and interpretation of a phenomenon, human experience, or text under investigation. This methodology focus fits well with the departure point of this work presented in 2.2. Both of these vast methodologies will be presented in the following sections. Earlier research of perceptual evaluation has used grounded theory (see section 2.5), which is a method for deriving a theory from the data, and is not a methodology in the sense of phenomenology or hermeneutics. However grounded theory is approach that will also be presented because of its previous use.

2.6.1 Phenomenology A phenomenology approach, which has it roots in a philosophical tradition, assumes there is a common essence or essences associated with an experience. The essences are the core meanings mutually understood through a phenomenon [33]. Moustakas [34] states that one can determine the underlying structures of an experience by interpreting the description of a situation where the experience occurs. Hence, by looking at the experience of several subjects, the phenomenon under investigation can become understandable to the researcher as well as to other uninitiated researchers. Typically, phenomenology research collects data using interviews, “phenomenological interviews”. These interviews use three processes in design and analysis: bracketing, phenomenological reduction, and horizontalization. Bracketing (epoche) is a process where the researcher, before starting the data collection, becomes aware of his/her prejudices, viewpoints, and assumptions. This self-reflective approach helps the researcher avoid interfering with the way the structure of the phenomenon is seen. Phenomenological reduction is the process of reading and describing several times to comprehend and understand the text under analysis. The next step, horizontalization, is a process closely related to phenomenological reduction. In this step, the researcher organizes the data into themes or clusters to make the essences of the investigated phenomenon visible [33].

2.6.2 Hermeneutics Hermeneutics looks at the human experience by studying the lifeworld2. Studying the lifeworld, the researcher can from a whole, including the description of experience [34]. Interpretation is a commonly used term in hermeneutics. The interpretation process or analysis process takes the form of a spiral called the hermeneutic circle/spiral. The researcher moves back and forth between individual elements (text segments) and the whole (entire text) [10]. This process is to make the intentions and meanings behind appearances in the text clear and understood [34] and to make one or several events understood in the context in which it is a part of [10]. $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$!$Q.21C5<;0R$S=;;$4-1$.@@10.=41$1>?1<.1381/A$=84.9.4.1/A$=30$8534=84/$4-=4$@=O1/$B?$4-1$C5<;0$52$=3$.30.9.0B=;$5<$85<?5<=41$;.21TD$!"#$%&'()*$+,")-./0$1-.2-(0/)34$5"2)-"6"*$+7)-8$9:4$;<9;$$


!"#!$ #)$

An important point is that phenomenology and hermeneutics are closely related. In phenomenology, there is a subset called “hermeneutic phenomenology”. This subset sees the world or experience as a text that must be read [10]. This division between the different methodologies is not sharp and there can be some overlap between them.

2.6.3 Grounded theory The main characteristic of grounded theory, as a method, is that the researcher should collect descriptive data before developing theories and hypotheses [10]. The focus is on generating a theory from the data and being grounded in data [29] using descriptions (e.g., interviews), conceptual ordering (e.g., coding), and theorizing (connecting concepts from the data). The researchers are encouraged to be creative in analysing the data. The term creative is determined by the researcher’s ability to name categories, ask stimulating questions, make comparisons, and extract an informative scheme that is grounded logically, systematically, and explanatory in a large amount of unorganized data [32]. To deal with large amounts of unorganized data, researchers use theoretical sampling. The researcher jointly collects, codes, and analyses the data. The coding process is called “open coding” and involves looking at the data and establishing segments consisting of behavioural actions and events, both observed and described. Constant comparison method is also used. This method consists of comparing one segment of data with another segment of data to look for similarities and differences. The similarities are sorted in a similar dimension, which are named. After some time, the dimensions become categories when no new dimensions can be identified [33].

2.6.4 Comparison of qualitative research The two methodologies – phenomenology and hermeneutics – collect descriptive data. This follows the viewpoint of perceptual audio evaluations where the subject is the central measuring device3 in which data are collected from. There is a difference between grounded theory and phenomenology: grounded theory creates a theory from the data and phenomenology begins with a theory. This indicates that grounded theory could be more fruitful when no preconceptions are involved (in theory) and when exploratory research is needed where low amount of information is known. With phenomenology, on the other hand, the researcher can investigate in detail the phenomenon by asking questions about the phenomenon and checking the results against a proposed theory in order to accept it or reject it. The phenomenology approach also resembles the departure point presented in 2.2 seen from an ontological view, where the objective world is obtained through our senses, seeing the objective world as a phenomenon that needs to be studied. Then it is feasible through a phenomenological approach to reach the objective world, at least in theory.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%$U134<=;$@1=/B<.3F$019.81$./$=$41<@$B/1$6:$JV+W$?D$#"(K$45$01/8<.61$4-1$;./4131</X$2B384.53$.3$=$;./413.3F$41/4D$$$


!"#!$ #*$

Seen from an analysis perspective, the hermeneutics approach is unavoidable, since it can shed light on the intentions and meanings that inform the listener’s descriptions, which could unravel interesting results from a listening test. In addition, the interpretation process of the analysis involves reading/studying the data several times, in parts and as a whole, in order to understand it.

2.7 Summary of theory and background The foundation of this work is based on the following: The only way to know anything about the objective world is to investigate how people perceive the world. Thus people make an interpretation of the experience of the objective world and this interpretation involves abstract thinking to a higher or lower degree. Perceptual audio evaluation methods can predict and generalize results. The research is experimental-oriented, making the application of the methods difficult in real-life/non-experimental conditions. Moreover, since the experiments conducted are often stringently controlled to avoid bias, no other information about the perception can be collected than the answer to the question under investigation. A qualitative approach is the opposite. It is situation-based research that collects a large amount of detailed information about a research topic. This can be done either by posing questions/theories before data collection starts or by deriving them from the collected data. However, there are several methodological implications when using a qualitative method in a field where quantitative praxis is common, such as perceptual audio evaluation, and the researcher should be aware of these implications. A mixed-method research employs different points of view incorporating both qualitative and quantitative methods to investigate successfully a research topic, both outside and inside the experimental conditions. In addition, it can collect detailed information and generalize results and methods.

3. Research question and focus 3.1 Motivation Perceptual audio evaluation methods have their merits in experimental conditions, where the researcher has control over variables and parameters under investigation. These methods, however, are not applicable in non-experimental and real-life situations where the researcher cannot control all variables, control what is happening, or guide participants to facilitate data collection, all primary concerns for evaluating perceptual audio methods. To evaluate perceptual audio quality in non-experimental and real-life situations, another method has to be applied. This raises the question: Can a qualitative method be applied successfully when evaluating perceived audio quality? A phenomenological approach is very interesting since its ontological and epistemological foundation resembles the goal outlined by perceptual audio evaluation: determining the underlying structure of experience. There is also evidence that mixed method research could be applicable because it can be used in experimental and non-


!"#!$ #+$

experimental conditions. Furthermore, data collection can be applied before, during, and after an experiment or non-experimental situation. A qualitative method also gives another aspect to a perception in comparison to a quantitative method. Using a mixed method approach, a researcher can collect two aspects of information about a research topic. This allows for an understanding of the perception that is broader than a single quantitative or qualitative research method could provide.

3.2 Aim This study investigates whether a phenomenological research method can be applied with success in the field of perceptual audio evaluation. The productiveness of a qualitative method is shown in the form of what type/types of information can be elicited and the usefulness and the relevance of the results elicited by the method. By looking at this information, an understanding of what the method can be useful for can be reached.

3.3 Objectives To accomplish the aim of this study, the main question (i.e., can a phenomenological method be successfully applied in perceptual audio evaluation research?) was broken down into sub-questions: • What information can this method generate? • Is the information/results collected relevant4 for the conducted study? • Can the information/results elicited be useful5? To answer each sub-question, the qualitative method was incorporated into three different studies (Papers 1, 2, and 3). Each study used a different design. This allows for the qualitative method to be examined in various conditions. It also gives insight into the relevance and usefulness of the results, and what type of information can be elicited for that particular study. In Figure 2, the study design is illustrated. Each study used a different design and had a different purpose and aim. The designs used were a non-experimental study, a study conducted in experimental conditions using a mixed method, and a study conducted in experimental conditions. Each study’s research topic is explained below. Paper 1 (Non-experimental study): Evaluate the sound and video quality of Master classes in classical music preformed over the public Internet using a qualitative method. $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$&$,-1$41<@$)"8"6/02$./$012.310$=/$V=07184.91WA$8;5/1;:$853318410$5<$=??<5?<.=41$45$4-1$@=441<$=4$-=30D$!"#$%&'()*$+,")-./0$1-.2-(0/)3$J!"#!Y"'Y!!KD$$'$,-1$41<@$=>"'=8$./$012.310$=/$V=07184.91W$=6;1$45$61$B/10$25<$=$?<=84.8=;$?B<?5/1$52$.3$/191<=;$C=:/?$!"#$%&'()*$+,")-./0$1-.2-(0/)3$J!"#!Y"'Y!!KD$


!"#!$ !"$

Paper 2 (Qualitative Method applied in addition to a standardized listening test): Investigate what additional data can be collected about the subjects’ perception using a qualitative method in a BS.1116 and MUSHRA designed listening test. Paper 3 (Study conducted under experimental conditions): Investigate what information can be collected using a qualitative method as the only data collecting method in an experimental condition (preference ranking listening test). Evaluation of a qualitative method applied in perceptual

audio evaluation research

Study 1: Qualitative

method applied in a non-

experimental situation (Paper 1)

Study 2: Experimental

condition using a mixed method approach: The

qualitative method applied to a standardized listening test

(Paper 2)

Study 3: Qualitative

method used in an experimental

situation (Paper 3)

Figure 2. Study design

4. Summary of papers 4.1 Paper 1 In Paper 1, the main goal was to evaluate perceived sound and video quality in a real-life/non-experimental situation – master classes of classical music taught via the Internet. To accommodate for this real-life situation in which the study was conducted, questionnaires and interviews were used as data collection methods. The questionnaires were analysed by looking at trends/major themes in the data and by using meaning categorization (inspired by Verbal Protocol Analysis). The analysis of interviews used meaning condensation to elicit the subjects’ perception. Both analysis methods are based in phenomenology and hermeneutics. The results from the questionnaire gave information about the following characteristics: • Sound quality: involving statements about the clarity and naturalness of the sound and

the easiness of understanding the teacher. • Asynchronicity between video and audio: involving statements about delay between

sites and delay between picture and video. • Teaching-related information: e.g., the teaching was excellent. • Communication-related information: e.g., the difficulties in student teacher

communication.


!"#!$ !#$

The meaning categorization gave a numerical overview of the general attitudes of the participants on the perception of sound, video, and teaching quality. Positive comments about teaching and sound quality were given the highest numeric value. Teaching, sound quality, and sound- and video-related were the most common comments. The interviews revealed several problems or issues. Perceived audio and video quality contained, for example, information about the subjects’ descriptions of their perception of sound and video quality by comparing it to known media, e.g., life-like MP3 and video quality better than a YouTube clip. The equipment’s limitations and student’s limitations could be separated from a teacher’s point of view. Perceived problems and potential problems with the equipment and teaching were also noted, for example, the delay between audio and video makes communication with students difficult, small details lost, and difficulty distinguishing the students’ playing technique. However, the equipment did allow for controlling tempo, intonation, articulation, and phrasing. Perceived differences and similarities with teaching master classes over the Internet were also noted. There were differences when correcting playing technique and creating a teacher/student relationship. However, using the equipment to meet with students seemed to be the same as meeting with students in, e.g., a teacher’s office. The results collected were detailed about perception of audio and video quality. The results also showed that the interaction between the subjects (teacher/student) was also a factor involved in creating the total judgment of quality.

4.2 Paper 2 Paper 2 had four main goals: 1) to investigate the amount of transparency reached for higher bit rates in DAB+; 2) to compare Swedish Radio FM and DAB+ chains in order to see how they relate to one another in quality; 3) to map the audio quality of the DAB+ and FM chains to the audio quality scale in the MUSHRA method; and 4) to investigate what further information can be elicited by applying interviews after the listening test. The fourth goal is the emphasis here. Two listening tests were conducted, one BS.1116 method and one MUSHRA method. After completion of both tests by a subject, an interview was conducted with each participant. A total of 25 interviews were conducted. Four of these interviews were randomly selected for further analysis using meaning condensation with a phenomenological point of departure. The main questions here were on what and how the subjects perceived the degradations. The results gave the following information: • What degradations were perceived, relating to, e.g., frequency response changes and

phase problems. • Which of the degradations were prominent, e.g., perception of stereo image and

frequency. • Which of the degradations were hard/easy to perceive – easy perceived degradations

such as noise levels and panned transients and hard degradations to perceive such as complex stimuli containing no reference points.


!"#!$ !!$

• What degradation gave low and high scores – low scores, e.g., large differences between the stimuli and the reference – and how much the degradations were audible in combination with how much they affected the results.

In addition to these results, information about the subjects’ comparison and evaluation strategies and perception of the scales used was collected.

4.3 Paper 3 Paper 3 addresses the issue of what goes into an affective judgment. To investigate this issue, a listening test experiment (researcher controlling all parameters) was designed and carried out. The objective of this research was to investigate the method and results to examine what information can be elicited from it. The subject’s task was to rank stimuli according to preference. The data collecting methods were interviews (two interview parts per subject). One initial interview was conducted before the test to collect information about each subject’s listening habits, attitudes, expectations, mood towards the test, and preference of music genre. A second interview was conducted during the listening test to collect information about what the subjects listen to, what differences they perceive, how or if they associate the perceived differences to something, and why they ranked the stimuli in this particular way. The analysis method used was meaning condensation. A total of three interviews were completed during the listening test. The results show information on several aspects of what goes into an affective judgment. • Method. What way does each subject listen after differences? The results showed

different strategies in listening after differences, e.g., listening to the whole, first impression, specific parts, listening beyond the music, and how well the verbal information came cross.

• Perceived differences. What differences does the subject perceive? The differences

could be divided into two categories, direct differences and associative differences. Direct differences involved, e.g., change in frequency content between stimuli, sharpness of consonants, changes in levels between instruments, changes in breathing sounds, and how well the verbal information came across. The differences based on association involved the subject to associate certain differences to pervious experience, e.g., radio commercials, how news or pop music “usually” sounds, or being placed at different locations in the mix.

• Ranking. How does each subject make rankings according to their affective judgment?

The ranking process was also divided into two main categories, ranking based on a combination of associated differences and direct differences, from above, and ranking based on association, e.g., ranking based on where the subject would listen to the stimuli (such as at home), one specific ranking, and doing a radio show, another ranking. From this part, an indication is given that listeners relate their associations to their background, e.g., the subject ranking in two separate ways as above also works with radio drama/radio education experience.


!"#!$ !%$

An interesting finding emerged when looking at the initial interview and the ranking order from one of the subjects. One subject ranked the stimulus with the lowest bitrate as most preferred, while the same subject’s listening habit was predominantly listening to audio with low bitrates. Thus a causal relationship between preference and listening habits was evident although further research on this topic is needed.

5. Summary of Results 5.1 Types of elicited information In summary, the information elicited from the qualitative method is related to the research topic of each study. The types of information elicited by the qualitative method from each study are summarized in Table 3. It can be seen from the results that the information elicited is not limited to the research question only and include other aspects: • Teaching- and communication-related issues [P1]; • Differences and similarities between distance music teaching and regular teaching [P1]; • Interaction (communication) between different parts is a large part of the total

perceived quality [P1]; • Perception on the usability of the scales, BS.1116-1 and MUSHRA, in the study [P2]; • Subjects’ attitudes towards the investigated study [P1 and P3]; and • Strategies/methods used by the subjects in the study [P2, P3].


!"#!$ !&$

Table 3: Summary of elicited information from studies.

Types of elicited information

Publication 1 Publication 2 Publication 3 • Info. about audio and video

quality perceived by the subjects (users).

• Teaching related info. • Communication related info. • Attitudes towards distance

music education. Perceived problems and possibilities with the equipment and teaching.

• Perceived differences and similarities in teaching.

• Interaction is a large part of the perceived total quality.

• Degradations perceived. • Degradations that were

prominent. • Degradations that were hard

and easily perceived. • How degradations

influenced high and low scores.

• Perception of the scales used.

• Evaluation strategies used by the subjects.

• Initial expectations/mood etc. of the subjects before the test.

• How the subjects listen for differences.

• What differences were perceived.

• What the subjects associated with the differences.

• How the subjects ranked and incorporated the perceived and associated differences in the ranking process.

• How the subjects incorporated pervious experience and background info. in the affective judgment, both when perceiving differences and ranking stimuli.

5.2 Relevant information for the study Paper 1 answers the research question that involves the evaluation of sound quality and video quality. In addition, paper 1 reveals other aspects about perceived quality. In paper 2, several types of information were relevant for the study – e.g., perceived differences, the degree of differences, and whether the differences are easy or hard to perceive. Paper 3 shows what an affective judgment is composed of and how the subjects carry out their affective judgments. This evaluation involves listener experience/background, direct differences, and associations with differences based on association and ranking based on differences.

5.3 Usefulness of the information The material below outlines the usefulness of the studies. • The information gathered increases the knowledge of what is being investigated as well

as adding new information previously unknown [P1, P2, and P3]. • The information gathered increases the accessibility to the subjects’ methods when

conducting a task, e.g., a listening test or affective judgment [P2 and P3]. • The information gathered about the method shows that the method can be useful in a

non-experimental context [P1], in an experimental context [P3], and in a mixed-method experimental context [P2].


!"#!$ !'$

6. Discussion 6.1 Relation to quality aspects The studies investigate different qualities: Paper 1 investigates the standard definition of quality (definition 3) used by standards [2] and P1, P2, and P3 investigates the “an event” quality definition (definition 4). The results of these investigations indicate that this particular qualitative method can bridge the gap between the regular definition of quality (definition 3) and the more subjective definition of quality (definition 4). It is also interesting to note that the studies incorporate several of the different abstractions levels presented by [6] (Table 2). The elicited information from the studies involves, e.g., perceptual properties (Auditive Quality), speech intelligibility (Aural-scene Quality), and assigning of meaning and dialog quality (Aural-communication Quality), further enforcing the notion that the information elicited is rich in details.

6.2 Framework of Validity and reliability Qualitative research should hold up to critical testing in order to make it valid and the researcher should strive to be as objective as possible (see section 2.3). In addition, it is very hard to come to the same results in different studies in qualitative research because the studies are situational. Each study presented here has fulfilled condition 1, posed by [11]:

In principle it is possible for anyone who has suitable intellectual capacity and technical equipment to critically test the results. ([11] p.113).

As such, qualitative studies are needed; however, such studies are difficult with respect to condition 2:

If several people were to critically test the results, all should be able to draw the same conclusions. ([11] p.113).

As stated before, these are major differences between a qualitative and quantitative approach. In quantitative research, one can easily reach condition 2, e.g., one can create the same conditions in which the experiment was conducted in, elicit a random sample out of the same target population of individuals, and use the same statistical analysis to interpret the same sets or new sets of data. The requirements of quantitative research – objectivity in the sense of reliability and repeatability, quantification of the impression, communication, and generalization [9] – cannot be applied to qualitative results. However, this does not mean that anything goes when it comes to qualitative research. Qualitative research focuses on the individual experience and how the individual perceives this experience. The subjects are picked because of certain criteria, e.g., experience of the phenomenon under investigation, which is called purposeful sampling. In a qualitative approach, the different viewpoints/aspects collected from the subjects give an understanding of the phenomenon under investigation. The qualitative results should be critically tested on their own terms and the research should address these main concerns: should fulfil condition 1 and strive for condition 2, should focus on the


!"#!$ !($

analysis, should represent the data collected, and should provide a different view or aspect about a phenomenon from study to study [10] and between each subject. This research has focused on phenomenological interviews and analyses. The method has been judged and critically tested based on qualitative scientific requirements. The studies have been the means for showing the usefulness of this method. The method has been critically tested in various forms, from non-experimental and experimental contexts. The methods used in each study have been described in a scientific manner that fulfils condition 1, and the analysis methods and the results have been scrutinized in each paper to reveal the applicability of the methods.

6.3 Interviews and analyses One underlying assumption of this work has been that there is a direct relationship between what is perceived and that is verbally said. With this assumption a difficulty arises: terms used could have different meanings for different subjects [1]. To adjust for this difficulty, the accuracy of interpretation of the interview data has to be high. This inflicts further difficulties such as interpretation accuracy. The phenomenological approach uses three steps to elicit results with accuracy: bracketing, phenomenological reduction, and horizontalization. As described previously, the bracketing is the process of having an objective approach towards the data collection and analysis. Thus the researcher avoids imposing any leading questions or any personal viewpoints when collecting and analysing the data. This is done using non-leading/open-ended questions in the interviews that allow the subject to respond to an interpretation made by the interviewer of a statement during the course of the interview. In the analysis, the bracketing becomes a bit harder. The interpretation process of the interview data, post interview, involves phenomenological reduction and horizontalization. These steps involve abstraction of the data, on several levels, to understand it. In direct results where the interviewee states a particular difference (e.g., too much low frequencies), a low level of abstraction is needed, but in more elaborated results such as ranking procedure or association to differences, the hermeneutical approach comes into play. In this case, the researcher reads parts of the transcribed interview and the whole several times and uses his/her knowledge to understand what the subject meant by a particular statement and this involves a higher degree of abstraction. This approach facilitates the collection of accurate data about a perceived phenomenon. A possible critique to this method, seen from a quantitative point of view, is the need for consensus between researchers. From a qualitative point of view, this criterion is not as important because if the results and analyses are thoroughly described, one should, in principle, be able to, with the same reasoning and the same data, draw the same conclusions.

6.4 Information elicited from the studies The qualitative data elicited in earlier research [28, 30, 31], which have been used in qualitative methods, and the data presented here [P1-P3] have similarities. The similarities indicate how a perceptual evaluation is constructed. [31] concluded that one could see in the results that sound evaluations depend on the social and cultural structures


!"#!$ !)$

in which an individual is imbedded. Knowledge about explored interdependencies could be used in future research to increase awareness of existence of environmental stimuli that can be harmful. [30] elicited several audio-visual quality experience factors from the qualitative data and showed that there is no 1:1 ratio between produced quality and experienced quality. [28] elicited even more detailed information about experienced quality factors from the qualitative data. The factors could be divided into high-level factors (content factors and usage factors) and low-level factors (audio, video, and audio-visual quality) and the connections between the factors. Paper 1 gives information about audio and video quality as well as teaching and communication factors that are involved in the quality judgment. Paper 2 shows what and how the differences are perceived and paper 3 gives detailed information about the composition and construction of an affective judgment. The perceptual research methods BS.1116 or MUSHRA [3,25] are limited to their scales. By using interviews in addition to these tests, as in paper 2, an understanding of how the scales are used and what subjects actually perceive can increase the knowledge of what is going on during the test. It also gives the researcher the ability to compensate when errors occur. One could argue that a conversation with the subjects after each session would suffice, but by using a qualitative approach the data can be scientifically analysed. The data/information collected from an interview or questionnaire depends on the type of questions stated. If the researcher does not follow up on interesting comments, the data will be limited. Showing a relationship between questions and answers is needed, so experience and training in interviewing can be essential for the researcher collecting the data. These qualitative methods can generate detailed information about an experience and a perception relevant to the study design. The information can be used to better understand the user experience of perception. This information can be highly valuable for any institution, company, or entity trying to understand and implement a new technology. In addition, the information generated from these types of qualitative methods can give insight from a relatively small population of subjects (e.g. from one to five) that can then be used to create larger quantitative experiments on different aspects of that particular experience.

6.5 Using the qualitative methods In conclusion, this study has shown that a qualitative approach using phenomenological interviews and analyses can elicit results that are valuable for the field of perceptual audio evaluation during experimental and non-experimental conditions. Thus this approach gives detailed information about the subjects’ experiences and perceptions both as a single methodology and in combination with quantitative research methods. This methodology allows the researcher to evaluate the subjects’ perceptions and extract what the perception is composed of and how it is used. This study also identifies several implications with using a qualitative method when studying perceptual audio: • Knowing the ontological stance and departure point of the qualitative method to

analyse and elicit information from the data; and


!"#!$ !*$

• Knowing the criteria a qualitative method is judged upon and how detailed the method and results are described.

In principal, it is possible to achieve the same result in the same setting.

6.6 Original contribution This thesis has applied qualitative research methods in the field of perceptual audio evaluation research. The method has been used in several different conditions to investigate its usefulness and relevance to the field. The investigation shows in detail several implications (ontological, epistemological, and methodological) associated with using this method in the perceptual audio evaluation field in which the researcher has to be aware of when using the qualitative method.

6.7 Further work Seen from a quantitative point of view, this method needs further evaluation: • Enhancing the analysis process by adding a second analysis conducted by a second

researcher before comparing the results from each analysis in order to check for consensus of the results and to further enhance the inter-subjectivity of the interpretation of the results; and

• Using participants with a larger age span or background so as to collect answers from a large population of individuals, an approach that could give information about a broader view of perception.

Seen from a qualitative point of view, further research can be done in the form of conducting each study once more with new subjects in order to see the phenomenon in a new perspective so as to add information about perception in these studies. The method can also be applied in other areas of research such as interaction-based applications and in an evaluation situation where the subjects cannot be interrupted such as when evaluating the interaction quality of an audio-visual system where subjects are interacting with each other via a piece of technology or when perceived quality of different graphical user interfaces (GUI) is evaluated where subjects are occupied with a particular task involving the GUI. The method can also be applied to evaluating the user quality, seen from the subject, of a listening test. This can facilitate a change in the design that could improve the listening test.

References 1. Berg, Jan; Rumsey, Francis. Identification of Quality Attributes of Spatial Audio by Repertory Grid Technique. Journal of Audio Engineering Society, Vol. 54, No. 5, May 2006 2. Martens, Harald; Martens, Magni. Multivariate Analysis of Quality, An introduction. John Wiley & Sons Ltd. Chichester, England, 2001, ISBN: 0-471-97428-5


!"#!$ !+$

3. BS.1116-1; Methods for the subjective assessment of small impairments in audio systems including multichannel sound systems; ITU-R Recommendations (1994-1997) 4. Lorho, G. Percived quality evaluation: an application to sound reproduction over headphones. PhD thesis, Aalto University, Espoo. 2010, ISBN: 978-952-60-3195-8 5. Jekosch, Ute. Basic Concepts and Terms of “Quality”, Reconsidered in the Context of Product-Sound Quality. Acta Acouticsa United with Acoustica, Vol. 90, p. 999-1006, 2004 6. Blauert, J; Jekosch, U; A Layer Model of Sound Quality; Journal of Audio Engineering, Vol 60, No !; January/February, 2012 7. Hansson, Sven Ove. The Art of Doing Science. Duplicated Writing (Compendium), 14

August, 2007 8. Patton, Michael Quinn. Qualitative Evaluation and Research Methods, Second Edition. SAGE publications, Inc. 1990, ISBN 0-8039-3779-2 9. Bech, Soren; Zacharov, Nick. Perceptual Audio Evaluation- Theory, Method and Application, John Wiley & Sons Ltd. Chichester, England, 2006, ISBN-13: 978-0-470-86923-9. 10. Tesch, Renata. Qualitative Research: analysis types and software tools. RoutledgeFalmer, 2 park Square, Milton Park, Abingdon, Oxon, Ox14 4RN, 1990, ISBN 1-85000-608-3. 11. Bergström, Lars. Objektivitet. Andra upplagan, Thales. 1987, ISBN 91-87172-16-x 12. Lawless, H.T.; Heymann, H. Sensory Evaluation of Food, Food Science Text Series, Springer Science + Business Media, LLC, 2010, ISBN: 978-1-4419-6488-5 13. Meilgaard, Morten; Civille, Gail Vance; Carr, B. Thomas. Sensory Evaluation Techniques, Fourth Edition. CRC Press, Taylor & Francis Group, LLC. 2006, ISBN-13: 978-0-8493-3839-7 14. Bradley, J. S.; Soulodre, G. A. Objective measures of listener envelopment. Journal of The Acoustical Society of America. Vol. 98, No. 5, November 1995 15. BS. 1387-1, Method for objective measurements of perceived audio quality. ITU-R Recommendations (1998-2001) 16. Ekeroot, Jonas; Berg, Jan; Nykänen, Arne. Selection of audio stimuli for listening tests. Presented at the 130th AES convention, 13-16 May, London, UK, 2011


!"#!$ %"$

17. Gabrielsson, Alf; Lindström, Björn. Perceived Sound Quality of High-Fidelity Loudspeakers, Journal of Audio Engineering Society, Vol.33, No !, pp. 33-53, January/February, 1985 18. Toole, Floyd E. Subjective Measurements of Loudspeaker Sound Quality and Listener Performance, Journal of Audio Engineering Society, Vol.33, No !, pp. 2-32 January/February, 1985 19. Ford, Natanya; Rumsey, Francis; Nind, Tim. Communicating listeners’ auditory spatial experiences: A method for developing a descriptive language. Presented at the 118th AES convention, 28-31 May, Barcelona, Spain, 2005 20. Blauert, Jens. Spatial Hearing: The psychophysics of Human Sound and Localization. The MIT press, Cambridge, Massachusetts, London, England, 1997, ISBN 0-262-02413-6 21. Martens, William L; Zacharov, Nick. Multidimensional Perceptual Unfolding of Spatially Processed Speech I: Deriving Stimulus Space Using INDSCAL. Presented at the 109th AES Convention, 22-25 September, Los Angles, California, USA, 2000 22. Koivuniemi, Kalle; Zacharov, Nick. Unraveling the perception of spatial sound reproduction: Techniques and experimental design. Presented at the 111th AES convention, 21-24 September, New York, NY, USA, 2001 23. Beresford, K; Ford, N; Rumsey, F; Zielinski, S. Contextual effects on sound quality judgments: listening room and automotive environments. Presented at the 120th AES convention, 20-23 May, Paris, France, 2006 24. Clark, David. High-Resolution Subjective Testing Using a Double-blind Comparator. Journal of Audio Engineering Society. Vol. 30, No. 5, May, 1982 25. BS.1534-1; Methods for the subjective assessment of intermediate quality level of coding system, ITU-R Recommendations (2001-2003) 26. Kjeldsen, Annie D. The measurement of Personal Preferences by Repertory Grid Technique. Presented at the 104th AES convention, 16-19 May, Amsterdam, 1998 26. Strohmeier, D; Jumisko-Pyykkö, S; Kunze, K. Open Profiling of Quality: A Mixed Method Approach to Understanding Multimodal Quality Perception, Hindawi Publishing Corporation, Advances in Multimedia, Vol 2010, Article ID 658980, 2010. 28. Jumisko-Pyykkö, Satu; Häkkinen, Jukka; Nyman, Göte. Experienced Quality Factors-Qualitative Evaluation Approach to Audiovisual Quality. Proceedings of SPIE - The International Society of Optical Engineering, 6507, art. No, 65070M, 2007


!"#!$ %#$

29. Glaser, Barney G.; Strauss, Anselm L. The Discovery of Grounded Theory: Strategies for qualitative research. Aldine Publishing Company, 200 Saw Mill River Road, Hawthorne, N.Y. 10532, 1967, ISBN 0-202-30260-1. 30. Jumisko-Pyykkö, S; Reiter, U; Weigel, Chr, Produced Quality is Not Perceived Quality - A Qualitative Approach to Overall Audiovisual Quality; 3DTV Conference, May 7-9, 2007 31. Schulte-Fortkamp, B; Fiebig, A; Soundscape Analysis in a Residential Area: An Evaluation of Noise and People’s Mind. Acta Acoustica United with Acustica. Vol. 92, p 875-880, 2006.

32. Strauss, Anselm L.; Corbin, Juliet. Basics of Qualitative Research- Techniques and Procedures for Developing Grounded Theory, Second Edition. SAGE Publications, 2455 Teller Road, Thousands Oaks California 91320, 1998, ISBN 0-8039-5939-7 33. Marriam, Sharan. B. Qualitative Research, A Guide to Design and Implementation. Jossey--!Bass, San Francisco, CA, US, 2009, ISBN: 978-0-470-28354-7 34. Moustakas, Clark E. Phenomenological research methods. SAGE publications, Inc. Thousand Oaks, California 91320, 1994, ISBN 0-8039-5799-8.

Paper 1: A qualitative approach to evaluation of perceived qualities of audio and video in a distance education context Submitted to: Acta Acustica united with Acustica, The Journal of the European Acoustics Association (EAA) – International Journal on Acoustics.

Nyberg, Berg A qualitative approach…

! "!

A qualitative approach to evaluation of perceived qualities of audio and video in a distance education context Dan Nyberg1 and Jan Berg1

1 Luleå University of Technology

Abstract This study presents a qualitative method for collecting and analysing data to describe audio and video quality. Used in the social sciences, arts, and humanities, this approach relies on phenomenology and hermeneutics and uses interviews and questionnaires to assess the audio and video quality of master classes in classical music taught via the Internet. Although this study is only exploratory, it provides evidence that the method could successfully be used to gather descriptions of perceptual qualities.


! "!

1. Introduction There are numerous contexts in which evaluation of audio quality may be performed and several evaluation methods have been developed over the years. Although each method has its own merits, specific methods may be more suitable in specific contexts. When a new context emerges, these methods need to be evaluated, refined, and considered. Existing methods, however, mainly rely on experimental data that comes from carefully manipulated situations to control experimental variables. Exclusively relying on experimental data may not actually reflect the reality that the experiment aims to represent. Relying only on experimental data can be an issue when both audio and video qualities are of interest and/or when other quality aspects are involved. In addition, most of these methods work by quantifying the listener’s experiences, an approach that limits the type of data collected. This limitation is imminent in the context of distance education over the Internet [1], including music education (MED), where research has increased over the last ten years [2,3]. Typically, a MED teacher located at one school teaches a student located at another school via the Internet, often by means of a video conferencing system [2,3]. Research efforts have been made to establish a transparent system for connecting two physical locations via a shared electronic virtual space for musical practice [4]. However, few studies specifically focus on how MED students and teachers perceive the audio quality of a particular system. Woszczyk et al. [4], for example, conclude that more aspects than audio quality are needed when evaluating such a system. This paper presents a selected qualitative method for data collection and data analysis that is commonly found in the social sciences, arts, and humanities and applies these methods to evaluate audio and video quality as it is perceived by teachers and students in a non-experimental distance education setting (specifically in a real-life classical music course conducted via the Internet). The terms audio and sound quality will be used interchangeable during the course of this paper. The term non-experimental means that the researcher has no control over the parameters such as choice of participants, where the study took place, transmission quality between the locations, and choice of equipment. The term real-life means that the lessons in the classical music course are authentic lessons in which the researcher does not intervene. These qualitative methods are used because the study was conducted in non-experimental and real-life conditions. Although the results from such a study may be used to evaluate the quality of audio equipment, this issue was not the target of this study. Rather, this study intends to investigate what type of data a researcher can collect by using these qualitative methods and to show the applicability of these methods in the field of perceptual quality. 2. Earlier research on perceived quality As stated in the introduction, MED involves more than one perceptual quality (e.g., audio and video). Previous research on the quality of perceived audio, which is one of the modalities under investigation, has been successfully evaluated. These data collection methods often deal with rating scales [5,6,7,8,9] and descriptive analyses and use the subject’s own vocabulary to evaluate the perceived audio quality [10]. There are also a variety of methods designed to evaluate perceived video quality. These data collection methods range from scales [11,12] to interpretation-based estimations of image quality [13] or both [14]. These approaches, however, only look at one modality. In multimodal research, the focus shifts since both audio and video are involved in a MED situation. In the multimodal field, several approaches are used to evaluate multimodal quality.


! "!

Some of the approaches use a mixed-methods approach where both numeric and descriptive data are analysed. For example, Strohmeier et al. [15] used a mixed-methods approach to evaluate multimodal quality perception of audio and video. They used a new method, Open Profiling of Quality, to understand quality perception. The Open Profiling of Quality contained three parts: a method they call psychoperceptual evaluation (the subject uses rating scales to evaluate perceived audio quality); sensory profiling (collecting individual quality attributes); and external preference mapping (constructing links between the psychoperceptual result and the sensory profiling). Using analysis of variance (ANOVA) and Principal Component Analysis (PCA), they analysed psychoperceptual evaluation and sensory profiling, respectively. Jumisko-Pyykkö et al. [16] used a qualitative approach to evaluate audio-visual quality and to test quality evaluation (single-stimulus method) and quality evaluation criteria (semi-structured interview). The data analysis combined a qualitative analysis based on grounded theory and a quantitative statistical analysis, Bayesian modelling. Jumisko-Pyykkö et al. [17] applied an entirely qualitative approach (semi-structured interviews) and compared the qualitative results with quantitative results in order to understand the relationship between produced and experienced quality in the context of interactive audio-visual application systems. The analysis used here also followed the grounded theory approach. Other researchers have employed single-method approaches [18,19,20,21] in multimodal quality research. Beerends and De Caluwe [18] tested the influence of video quality on perceived audio quality (and vice versa) using a nine-point absolute category rating scale. The data analyses were made using ANOVA. Bech et al. [20] analysed the interaction between audio and visual factors in the context of a home theatre system. The experiment collected numeric data from rating scales using defined attributes with anchors and the data were analysed using ANOVA. All methods presented above have shown to be well suited for the intended purposes and delivered valuable results; however, these methods are designed for experimental or laboratory contexts and some of the methods could be hard to adapt to a real-life situation (non-experimental situation) such as the MED situation. One cannot control all parameters involved in an MED situation, contrary to the experimental research presented above. Clearly, laboratory testing cannot completely account for the real life situations found in distant learning MED, a situation that is relatively unstudied and may require new methods to develop a complete picture of its usefulness. 3. A qualitative approach When considering a suitable method in a relatively new, non-experimental, and multi-modal context, such as master classes in classical music over the internet, another foundation may be required that uses qualitative methods that focus more on describing and understanding the experience than on quantifying it. This approach makes it possible to adapt and employ methods used in other fields, in particular methods used in the humanities, social sciences, and psychology. Viewing a research area from a different research foundation can been fruitful; for example, Blauert and Jekosch [22] took a perceptionism view of the world, a view that relies on the belief that all knowledge is based on sense perception. This view allowed them to create a layer model of audio quality.


! "!

The following sections present the implications of using a qualitative approach and points of departure for qualitative research as well as descriptions and applications of the data and analysis methods. 3.1 Implications of using a qualitative approach When researchers use qualitative methods, they need to consider the possible limitations of such an approach. In qualitative research, the question of validity focuses on the employment of the “reduction” process (analysis) that leads to a result and not whether the data can be replicated, a common view issue in the field of audio quality research. Thus qualitative research focuses on how clearly the analysis is conducted and described by the researcher. The important point of qualitative research is that two researchers do not produce exactly the same results when faced with the same task. This poses no problem, however. That is, competent and skilful researchers produce results easily recognized by other researchers. Each exploration/study should bring a different perspective on the phenomenon under study and each perspective creates a more comprehensive picture of the phenomenon [23]. 3.2 Qualitative points of departure Empirical phenomenology and hermeneutics, approaches frequently used in psychology [23, 24], are suitable points of departure for evaluating audio and video quality in a distance education context such as MED conducted via the Internet. Empirical phenomenology has the following goal:

To understand the general psychological meaning of some particular human way of being-in-a-situation [. . .] through a number of descriptions of this way of being-in-a-situation from people who have lived through and experience themselves as so involved. [23] (p. 40)

Moustakas, agreeing with Tesch [23], states that one can determine the underlying structures of an experience by interpreting the description of a situation where the experience occurs [24]. As for hermeneutics, Moustakas summarizes it this way:

[R]eading a text1 so that the intention and meaning behind appearances are fully understood. [24] (p. 9)

This view agrees with Tesch’s explanation of hermeneutics [23]. Hermeneutics looks at human experience by studying the lifeworld2 in order to form a whole, including a description of experience. Interpretation of an experience shows what is hidden behind an phenomenon [24]. This understanding means that an empirical phenomenological and hermeneutic departure may help evaluate audio quality, video quality, and their interaction in order to extract, interpret, and describe the experience of the persons involved in a MED situation. Other researchers have already applied grounded theory to evaluate the perception of audio-visual quality [16, 17] as a way to understand the characteristics of multimodal quality. Grounded theory, an approach that is often found in sociology, generates a theory from data [25,26]. When conducting grounded theory research, a researcher should collect data before developing theories and hypotheses rather than developing theories and hypotheses before collecting data [23].

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!$%&!'&()!*'&+',!-.!/!%&()&.&0'-1!2&.2&!-2!3&(4!5-6&!/.6!-.17(87(/'&2!%0)/.!/1'-7.9!-.'&(3-&5!'(/.21(-8'29!/.6!'&+'2!:;<=>!;!?-@&57(A6!-2!/!'(/.2A/'-7.!@(7)!B&()/.9!!"#"$%&"'(C!,DAA!'%&!-))&6-/'&!&+8&(-&.1&29!/1'-3-'-&29!/.6!17.'/1'2!'%/'!)/E&!08!'%&!57(A6!7@!/.!-.6-3-60/A!7(!17(87(/'&!A-@&,)*+"&),-./01)23"0456$)745(4/$6089>!


! "!

Grounded theory has similarities to the proposed approach above, as we want the data to give information to us about a phenomenon (in this case audio and video quality and interaction between teacher and students), but our purpose is not to generate a theory or a hypothesis about the phenomenon; rather our focus is to describe the perception of what is experienced in this specific context. 3.3 Collection of data The two data collection methods used, questionnaires and interviews, can be applied post the MED situation, thus they do not influence student-teacher interaction. Both these methods are often found in qualitative research. Interviews are used in phenomenology research and to some extent in hermeneutics [23, 24, 27]. Compared to numeric-based data collection methods, both questionnaires and interviews can provide detailed descriptions of how audio and video quality are perceived, separately and together. Similarly, questionnaires have been used in earlier studies [28,29,30,31] with good results. Open-ended questionnaires can gather many responses from the subjects and give an overview of the field under study. Of course, questionnaires can contain questions where the responses are quantitative, e.g., by using scales, lines, checkboxes, etc., but such approaches are not considered here as they represent a methodology outside the scope of this paper. Interviews provide insight into how subjects experience the learning environment – audio, video, and student-teacher interaction. This approach also allows more flexibility in data collection because follow-up questions can be asked [27], which is not possible with questionnaires. Collecting descriptive data from interviews could be beneficial when the research field is relatively unexplored [16]. The following sections show how these data collection methods are applied in a MED context. 3.3.1 Using questionnaires Two sets of questionnaires were distributed during several MED sessions. Questionnaire 1 is a brief questionnaire containing a set of open-ended questions. Based on the answers collected from the first questionnaire, not presented here, a second more elaborated questionnaire was designed (Questionnaire 2), see appendix 1, with more specific open-ended questions. The new set of open-ended questions was designed to encourage the participants to reflect on whether they perceived the audio quality and video quality as good or bad and why they perceived the qualities in these particular ways. The questions were designed to capture a comprehensible view of the participant’s perception of the overall quality of the MED system and the participant’s experience. Data from Questionnaire 2 were systematically analysed (described below). 3.3.2 Using interviews As a next step, personal semi-structured interviews with the participants in the MED situation (students and teachers) were used to further shed light on the results from the questionnaire and on perceived quality. An interview guide with open-ended questions was used to obtain vital descriptions of the users’ experience [24]; see Appendix 2 for the questions used in the interview guide. 3.4 Analysis of the collected data A phenomenological and hermeneutical approach is used as the point of departure for the analysis. The analysis focuses on describing the user’s perception of audio and video quality as well as the interaction between teachers and students. This section presents the data analysis methods.


! "!

3.4.1 Using questionnaire analysis An ad-hoc analysis is used on the data and includes theme analysis and meaning categorization analysis [27,32]. In the theme analysis, themes are searched for in the collected data [32]. The theme analysis is accomplished by first listing all the collected answers under each question and reading all the answers for similarities and common answers [19]. This part of the analysis is analysed hermeneutically. The meaning categorization analysis is an analysis that is inspired by Verbal Protocol Analysis [33,34]. Each answer is coded and counted according to its identified properties. The verbal protocol analysis requires an algorithm or description on how to handle each verbal unit [33, 34]. Berg [33] categorised the data into descriptive features and attitudinal features. Each category was then divided into two subsets. For descriptive features, the units were divided into unimodal (only audio modality) and polymodal (other sensory modalities). The attitudinal features were divided in the same way but into emotional/evaluative attitudes or attitudes related to naturalness. The current analysis, however, differs from Berg’s approach: each answer to the open-ended questions for both its descriptive and its attitudinal features was analysed as each answer may contain both. Both the descriptive and attitudinal features of each answer were interpreted based on the context (each question) from which they were taken. The sorting processes were conducted in the following way: Sorting 1 – each answer was sorted after its interpreted descriptive feature using the labels below; e.g., if the answer were related to audio quality, it was given a sound quality-related label (Sqr); and Sorting 2 – each answer was sorted after its interpreted attitudes, i.e., positive, negative, both, or blank. The following labels were used when sorting each answer into its respective topic and attitude:

Sorting 1. Descriptive features: Sound quality related (Sqr), video quality related (Vqr), sound and video quality related (Sqr&Vqr), communication related (Com) (“communication” here refers to speaking and interaction among the participants), teaching related (Tch), technology related (Tec), teaching and communication related (Tch&Com), and diverse statements (Div). Sorting 2. Attitudinal features: positive (+), negative (-), positive and negative (+ & -), and attitudes that contained no positive or negative statements (blank).

Consequently, sorting 1 in the meaning categorization analysis provides an overview of the number of answers on each perceived quality. The second sorting (Sorting 2) provides an overview of the answers’ attitudes (sorting 2): positive, negative, both, or blank (general statements). 3.4.2 Using interview analysis The interview data were analysed using an approach commonly used in phenomenology studies [23, 27], meaning condensation [27]. One or several persons can do this type of analysis. When looking at the data from a phenomenological point of view, the interviewer and/or researcher must set aside his/her pre-understanding of the phenomenon to obtain objectively rich and clear descriptions of the phenomenon under study [27]. This is referred to as “bracketing”. The bracketing started during the design of the interview questions by formulating open-ended questions to facilitate the subjects’ own descriptions of their experience and continued throughout the analysis process.


! "!

Meaning condensation concentrates the uttered meaning into more essential meanings and contains five general steps: Step 1 – reading the interview to obtain an overview of its content, establishing a sense of the whole; Step 2 – creating units of meaning (the answers) as the interview subject expresses them; Step 3 – creating themes that dominate the units of meaning; Step 4 – asking questions to the units of meaning based on the research purpose; and Step 5 – creating a summary of the interviews’ central themes and presenting them in one descriptive statement per interview [23, 27]. The steps of the interview analysis used the original method as a baseline for the analysis. The steps used in our analysis are listed below:

1. Transcribing the interview (transcription methods used were structured in colloquial

language [27]); 2. Creating units of meaning of each transcribed answer; 3. Creating themes; 4. Sorting the units’ answers into corresponding themes; and 5. Summarizing each theme in the text for each interview.

All steps of the evaluation method are presented in a block diagram in Figure 1. Each step (block) is followed by its result. 4. Evaluation of perceived qualities of a master class The qualitative approach presented in section 3 was developed to evaluate perceived audio quality during real MED situations while considering the perceived video quality and interaction between the teacher and student. The MED situations were real master classes in classical music taught over distance and connected via video conferencing systems and an IP network (Public Internet). That is, the teacher was at one location and the student at another. This section presents the results from the qualitative approach. The study was conducted under non-experimental conditions. Measurements of latency between the location and audio and video were not possible due to inaccessibility to measurement equipment. Appendix 3 provides information about what equipment was used at each location. 4.1 Background to the study The master classes were conducted in Oulu (Finland), Helsinki (Finland), Piteå (Sweden), Olos (Finland), and Rovaniemi (Finland) on several occasions during the autumn and winter of 2009 and spring and autumn of 2010. The instruments/ensembles used in these master classes were violin, French horn, cello, and string quartet. Singing was also a part of some classes. 4.2 Method used for the evaluation The steps used for evaluation of the master classes were as follows:

#$ Questionnaire 1 (Brief); %$ Questionnaire 2 (Extended); &$ Ad-hoc analysis of the questionnaires; '$ Interviews; and ($ Analysis of the interviews (meaning condensation).


! "!

Both questionnaires were completed and the interviews were conducted after each lesson so as not to interrupt the performance of the teachers and students. The time between distributing the questionnaires and conducting the interviews after each master class was no longer than one hour. During the first master class, Questionnaire 1 (three open-ended questions) was distributed to obtain a general overview of the participants’ experiences: their evaluation of the overall sound quality and their evaluation of their overall distance learning experience. A total of six questionnaires were collected. From this information, a second questionnaire was designed and distributed to collect more detailed descriptions from the participants. The questionnaires were distributed to all participants (teachers, students, and observers participating in the master classes). A total of 22 questionnaires were collected over a period of three master classes. After the distribution of the questionnaires, a total of six interviews were conducted: two interviews with students participating in the master classes and this master class study and four interviews with two master class teachers. (One of the teachers was interviewed three of the four times in order to collect additional information from each session.) The second teacher was interviewed via e-mail and several follow-up e-mails were sent to encourage further reflection on topics deemed interesting by the interviewer thus the same procedure as in a regular interview was used. 4.3 Analysis One person conducted the analyses of the questionnaire data and interview data, using traditional data analysis methods. The analyses were carried out using the methods presented in section 3.4. 4.4 Results from the analysis 4.4.1 Questionnaire analysis (ad-hoc analysis) The first part of the analysis, finding major trends, gave four major themes/trends. The sound quality-related trend contained statements on the perception of the sound. Several answers stated that the instruction and music examples played from the teacher/student were perceived as good and clear:

“Good natural sound”. “Sound was very clear; there were no problems understanding what the teacher said”.

The sound and video quality-related trend included the perception of an asynchronicity between audio and video:

“The delay could be shorter”. “The sound and the picture should be in the same time”.

The teaching-related trend showed that the participants thought that the distance master classes offered more opportunities for participating in master classes and that they offered the


! "!

opportunity to play in front of different teachers. This trend also meant travel was not required:

“The teaching was excellent”. “You don’t have to travel far away to get lessons”.

The communication-related trend contained the perception of having a hard time communicating with the student or the teacher and playing together with the teacher:

“It is more difficult to communicate with the teacher”. “There should be a clear signal to the students so that they would know when to stop playing”.

The second part of the ad-hoc analysis – meaning categorisation analysis (Table 1) – shows the total number of responses of the categories. Table 1 also shows the distribution of the received answers: the results show that the most frequently occurring responses are positive and are related to teaching. The second largest quantity of responses is positive and related to sound quality. The third largest quantity of responses does not contain any interpreted attitudes of positive, negative, both, or general statements (blank). These qualities are related to sound and video quality. The data also show that positive attitudes are most common. The second most common attitude is general statements without any positive/negative or both attitudes in the answers. The third most common attitude is negative and the fourth most common contains both positive and negative attitudes. 4.4.2 Interview analysis (meaning condensation) The descriptive texts collected from each interview (step 5, Interview analysis) are summarized in this section into corresponding themes, i.e., making one descriptive text/ description for each theme based on all interviews. This strategy is done to maintain the richness of the information in the collected data. A total of three themes were created. Perceived audio and video quality The teachers perceive that the video quality is good and works for distance master classes. The teachers could also imagine how the sound of each instrument sounded live, based on experience, even though the instruments did not sound natural during the master class. One teacher perceived the sound quality as “metallic” and “boring”, but with significant direct sound, no room sound, and good dynamics. The same teacher compared the sound to a high fidelity MP3. One teacher perceived a delay between audio and video, with the audio leading. With respect to sound quality, one teacher found it hard to distinguish between the system’s limitations and the student’s limitations although the teacher could distinguish this difference in a later master class. One teacher could easily see the student’s playing technique, but could not evaluate it because the sound was difficult to hear. Both students compared the video quality as equal to or better than a YouTube clip. One student could only see the main features but not the contours or the proportions of the image. One student perceived the audio quality as “far away, distant, and a little muddy” and compared the audio to a YouTube clip. In addition, the same student found it hard to understand the teacher’s voice. One student compared the sound quality to a MP3 coded sound, but worse than a movie although the student could distinguish between a normal


! "#!

spoken voice and a softly spoken voice. One student perceived the delay between audio and video as strange. Perceived problems and possibilities The delay between audio and video and the locations are perceived as a problem that makes it hard to communicate with the teacher/student. Small details in the music disappeared. Not knowing what is sent to the other locations, both related to video and sound quality, is perceived as a problem. During a distance master class, it is also hard to perceive the playing technique used by the students and how the students control their muscles. Distance learning is also problematic for music classes because it is difficult for teachers to evaluate their students’ playing techniques and muscle control. Several topics – controlling tempo, intonation, articulation, and phrasing – were adequately dealt with during the classes. According to one teacher, if the delays between audio and video were short and the teachers were aware of it, the delay could be less problematic. Both teachers perceived the technology in an overall positive light. One student stated that physical presence, i.e., meeting in real life, is preferred over distance master classes, but distance master classes provide the opportunity to contact people who are based far away and to obtain new input and comments from other teachers. One teacher offered a similar evaluation. One teacher stated that there are indirect benefits for the master classes: saves time, travel, and money. Perceived differences and similarities between regular and distance master classes The perceived similarities were meeting with the student personally and discussions with the student. In addition, from a pedagogic point of view, the distant learning master class was similar to a regular master class. Two differences were identified: correcting playing technique was difficult and verbal explanations rather than hands-on demonstrations were required to explain new positions to the student, a situation that required more time. In addition, creating a relationship with a teacher/student during distance master classes was more difficult than during a regular master class. One teacher also stated that a physical/personal contact established before starting long periods of distance lessons would be helpful. 5. Discussion 5.1 The results Seen together, the ad-hoc analysis and the meaning condensation analysis provided an overview of what type of trends/themes exist in the data and how the perceived audio quality relates to the perceived video quality and to the interaction between the users. Masum et al. found that teachers and students found the tools (system used) comfortable [2]. This finding coincides with our results; the subjects could complete the master classes without any major difficulties. Our results also indicate that the perceived audio and video quality in the system is not optimal but is sufficient for this type of music education. There are also problems with lack of synchronicity between video and audio and latency between the locations, but the subjects’ statements indicate that they can work around these problems and manage a MED situation successfully. Woszczyk et al. also report similar results [4].


! ""!

As a side note, the results from our study also align with previous research when it comes to teaching. To conduct successful teaching with the use of video conferencing systems, the teacher needs to adapt the content to handle the pedagogical situation [1]. In addition, it is cost-effective to bring teachers and teaching experiences to a large population of students [1]. Clearly, videoconference systems also allow teachers to train and teach in places other than their home location [2]. All these results coincide with our results. The subjects’ initial attitude towards using such a system could be a bias that affected the results. That is, a student may have entered the study with a preconceived idea about distance learning. Such preconceived attitudes need to be considered when dealing with subjective responses. Before the study, based on their experience of sound quality, the authors expected the subjects to be negative about the sound quality; however, the results did not indicate this. Another possible bias, which may be connected to the positive attitudes collected, is that all the participants volunteered to participate in the study, showing they had an initial interest in MED; that is, the participants were self-selected on some level. 5.2 The methodology Using qualitative methods including analysis shows a potential for arriving at a set of data that can be usable when evaluating several perceived qualities in one system. This method, with post questionnaires and post interviews, allows the participants to complete their task without interruptions and encourages the subjects to use their own words to describe their experience. This approach helps create a broad picture of what is happening in the study. This method also allows for a simultaneous description of what is perceived and what is affected even if there are more parameters affecting the perceived total quality than just the audio quality. Comparing this method to other subjective assessment methods of audio quality proposed by other studies [7], one could not say a particular method is better than another method because they collect different sets of data; however, some subjective assessment methods [5,6,7,8,9,11] have predetermined verbal descriptors and factors that aim at a defined part of the perceived quality. The approach in this paper enables the subjects to reflect on what they have perceived with few restrictions. Thus, the information can shed light on broader aspects of the perceived quality. This broad approach makes it possible to discover unexpected and possibly important factors, related either to audio quality or to other qualities, that affect how a specific situation or implementation is perceived. Hence, factors outside the audio domain may also be considered. On the topic of analysis the data presented in Table 1, do not say anything in detail about the content of the responses; the data only show the distribution of answers, which demonstrates that simple quantitative observations can also be made using this method. One person, as stated in the analysis section, analysed the questionnaires and interviews. Adding a second or several other researchers’ analyses and comparing them to each other could enhance the results further, as this allows checking the analyses for co-researcher agreement. By using open-ended questions in a questionnaire, a variety of answers can be elicited. This strategy can be an advantage if a participant gives an answer that sheds light on a new area that the researcher has overlooked, or it can be fruitless if the participants “don’t know” or do not even answer the question. Open-ended questions used during an interview can encourage the interviewee to answer freely while still allowing the interviewer to guide the interviewee


! "#!

into interesting topics if they arise. In the latter situation, the interviewer needs to be very responsive to follow-up possibilities and needs to understand what to ignore. Such a strategy, of course, is a source of bias that has to be considered carefully. Researchers using questionnaires and conducting several interviews during an evaluation process must be aware that the data size quickly becomes large and is hard to manage and time-consuming to analyse. In the meaning condensation of the interviews, each unit of meaning was categorized into themes. This division made the answers more clear and provided a better overview and a better understanding of each interviewed subject’s perception. As all data collection was done after the completion of the event, this collection method relies on the subject’s memory. When answering questions after an event rather than during it, some shift in the perceived sensations may occur. This shift can be a disadvantage compared to other more direct methods used for rating and assessing. Small differences between sessions may not be captured, as they would be harder to detect when one experience is compared to another with a time gap in between. Because the study was conducted during a real MED situation, some other limitations did arise. One limitation of the methodology is that only one person conducted the coding. However, when using a phenomenological or hermeneutical approach for the analysis, the researcher has to be aware of his/her own preconceptions and prejudices and exclude them during the analysis. The author performing the analysis was aware of this possible bias. As can be seen in section 4, appendix 3, the equipment was changed occasionally between different locations and sessions. This can, of course, result in a bias, but the primary focus was not to link a set of experiences to particular equipment, but to evaluate the perceived audio and video quality in an ecological valid situation during live distance master classes. Another restriction was the unavailability of measuring the delay between the locations and between audio and video. This could have shed some light on when delay was present and when the delay was not present. As indicated, the approach used in the current study yields different information compared to most of the previously used methods that evaluate audio quality. Hence, this method cannot be used interchangeably with existing methods to obtain the same type of data. However, by adding information that is not available from other methods, this approach will increase the knowledge of the subject’s experience. The results may also be used in an exploratory way as a means of observing what subjects perceive as noticeable, which in its turn can be used to develop evaluation scales used in existing methods. The rich verbal data resulting from a qualitative approach may provide a more holistic representation of an audio event, improving our understanding of how the event is experienced. 6. Acknowledgements This work is part of the Vi R Music project and was partially financed by the European Union program Interreg IV A Nord. The authors would like to thank all the participants, students, and teachers who participated in the surveys and interviews. A thanks also goes to colleagues, technicians, and staff members who made this work possible. The authors also want to express a special thanks to Dr. Anders Persson, Jonas Ekeroot, and Jon Allan for their valuable comments on the drafts.


! "#!

References 1. Greenberg, Alan; Russ Colbert; Navigating the Sea of Research on Videoconferencing-Based Distance Education. Wainhouse Research, Polycom, Inc. February 2004 [Cited: 2010-09-20] URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.110.8045&rep=rep1&type=pdf 2. Masum, Hassan; Brooks, Martin; Spence, John; MusicGrid: A case study in broadband video collaboration. [Cited: 2009-10-22] URL: http://131.193.153.231/www/issues/issue10_5/masum/index.html 3. Shepard, Brian K; Howe, Gregory; Snook, Tom. Internet2 and musical applications, Proceedings, National Association of schools of music, The 84th annual meeting, USA, Seattle, November 24, 2008. URL: http://www.briankshepard.com/pdf/2008Proceedings.pdf 4. Woszczyk, Wieslaw., Cooperstock, Jeremy., Roston, John., Martens, William. Shake, Rattle, and Roll: Getting Immersed In Multisensory, Interactive Music Via Broadband Networks, Journal of Audio Engineering Societ, Vol.53, No 4, pp. 336-344, April, 2005 5. Toole, Floyd E. Subjective Measurements of Loudspeaker Sound Quality and Listener Performance, Journal of Audio Engineering Society, Vol.33, No !, pp. 2-32 January/February, 1985 6. Gabrielsson, Alf; Lindström, Björn. Perceived Sound Quality of High-Fidelity Loudspeakers, Journal of Audio Engineering Society, Vol.33, No !, pp. 33-53, January/February, 1985 7. Bech, Soren; Zacharov, Nick. Perceptual Audio Evaluation- Theory, Method and Application, John Wiley & Sons Ltd. Chichester, England, 2006, ISBN-13: 978-0-470-86923-9. 8. BS.1116-1; Methods for the subjective assessment of small impairments in audio systems including multichannel sound systems; ITU-R Recommendations (1994-1997) 9. BS.1534-1; Methods for the subjective assessment of intermediate quality level of coding system, ITU-R Recommendations (2001-2003) 10. Lorho, Gaëtan: Individual Vocabulary Profiling of Spatial Enhancement Systems for Stereo Headphone Reproduction, Presented at the 119th AES convention, New York, New York USA, October 7-10, 2005. 11. BT.500-12; Methodology for the subjective assessment of the quality of television pictures, ITU-R Recommendations (09/2009) 12. Seshadrianthan, K; Soundararajan, R; Bovik, A, C.; Cormack L, K; Study of Subjective and Objective Quality Assessment of Video; IEEE transactions on image processing, Vol 19, No 6; June 2010


! "#!

13. Radun, Jenni; Leisti, Tuomas; Häkkinen, Jukka; Ojanen, Harri; Olives, Jean-Luc; Vuori, Tero; Nyman, Göte. Content and Quality: Interpretation-Based Estimation of Image Quality, ACM Transactions on Applied Perception, Vol 4, No. 4, Article 21, January, 2008 14. Nyman, G et al.; What do users really perceive – probing the subjective image quality; Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol 6059, 605902; 2006 15. Strohmeier, D; Jumisko-Pyykkö, S; Kunze, K. Open Profiling of Quality: A Mixed Method Approach to Understanding Multimodal Quality Perception, Hindawi Publishing Corporation, Advances in Multimedia, Vol 2010, Article ID 658980, 2010. 16. Jumisko-Pyykkö, Satu; Häkkinen, Jukka; Nyman, Göte. Experienced Quality Factors-Qualitative Evaluation Approach to Audiovisual Quality. Proceedings of SPIE - The International Society of Optical Engineering, 6507, art. No, 65070M, 2007 17. Jumisko-Pyykkö, S; Reiter, U; Weigel, Chr, Produced Quality is Not Perceived Quality - A Qualitative Approach to Overall Audiovisual Quality; 3DTV Conference, May 7-9, 2007 18. Beerends, John G., De Caluwe, Frank E.; The influence of Video Quality on Perceived Audio Quality and Vice Versa; Journal of Audio Engineering Society, Vol 47, No 5; May 1999 19. Hollier, M.P.; Rimell, A.N.; An Experimental Investigation into Multi-Modal Synchronisation Sensitivity for Perceptual Model Development, Presented at the 105th AES Convention, Preprint, San Francisco, CA, September 26-29, 1998 20. Bech, Soren; Hansen, Villy; Woszczyk, Wieslaw; Interaction Between Audio-Visual Factors in a Home Theatre System: Experimental Results; Presented at the 99th AES Convention, Preprint, New York, October 6-9, 1995 21. Häkkinen, Jukka; Alatonen, Viljakaisa; Schrader, Martin; Nyman, Göte; Lehtonen, Miikka; Takatalo, Jari; Qualitative analysis of mediated communication experience; Quality of Multimedia Experience (QoMEX), Second International workshop, June 2010 22. Blauert, J; Jekosch, U; A Layer Model of Sound Quality; Journal of Audio Engineering, Vol 60, No !; January/February, 2012 23. Tesch, Renata. Qualitative Research: analysis types and software tools. RoutledgeFalmer, 2 park Square, Milton Park, Abingdon, Oxon, Ox14 4RN, 1990, ISBN 1-85000-608-3. 24. Moustakas, Clark E. Phenomenological research methods. SAGE publications, Inc. Thousand Oaks, California 91320, 1994, ISBN 0-8039-5799-8. 25. Glaser, Barney G.; Strauss, Anselm L. The Discovery of Grounded Theory: Strategies for qualitative research. Aldine Publishing Company, 200 Saw Mill River Road, Hawthorne, N.Y. 10532, 1967, ISBN 0-202-30260-1. 26. Strauss, Anselm L.; Corbin, Juliet. Basics of Qualitative Research- Techniques and Procedures for Developing Grounded Theory, Second Edition. SAGE Publications, 2455 Teller Road, Thousands Oaks California 91320, 1998, ISBN 0-8039-5939-7.


! "#!

27. Kvale, Steinar; Brinkmann, Svend. InterViews - Learning the Craft of Qualitative Research Interviewing, Second Edition. SAGE Publications, Inc. 2455 Teller Road, Thousand Oaks, California 91320, 2009, ISBN 978-0-7619-2541-5. 28. Fang, X.; Chan, S.; Brzezinski, J.; Nair, C.; Development of an Instrument to Measure Enjoyment of Computer Game Play; International Journal of Human-Computer interaction, vol. 26, No 9, pp 868-886. 2010 29. Wolfson, S.; Case, G.; The effect of sound and colour on responses to a computer game; Interacting with computers 13, pp. 183-192; 2000. 30. Nacke, Lennart E.; Grimshaw, Mark N.; Lindley, Craig A.; More then a feeling: Measurement of sonic user experience and psychophysiology in a first-person shooter game; Interacting with computers 22, pp. 336-343; 2010 31. Hsu, Shang Hwa; Wen, Ming-Hui; Wu, Muh-Cherng; Exploring user experiences as predictors of MMORPG addiction; Computers & Education 53, pp. 990-999, 2009 32. Ejlertsson, Göran; Enkät i praktiken – en handbook i enkätmetodik (andra upplagan); Studentlitteratur, 2005; ISBN; 91-44-03164-5 33. Berg, Jan. Systematic Evaluation of Perceived Spatial Quality in Surround Sound Systems; Publication 2 pp. 3-4; Doctoral dissertation, Department of Music and Media, Piteå, Sweden, 2002. ISSN: 1402-1544; ISRN: LTU-DT—02/17—SE 34. Samoyleko, E.; McAdams, S.; Nosulenko, V. Systematic Analysis of Verbalisation Produced in Comparing Musical Timbres; International Journal of Psychology, vol. 31; pp. 255-278, 1996.


! "#!

Figure 1. The steps and the results for each step used in the evaluation method.

!"#$%&''()*#+,+-.*)#/0+

!"#$%&''()*#+1+-234#'5#50+

67#8#+('(9:$)$+ ;#(')'<+=(4#<&*)>(%&'+

('(9:$)$++

?'4#*@)#A$+

;#(')'<+=&'5#'$(%&'+('(9:$)$+

B5C7&=+('(9:$)$+

!"#"$%&'()"$)*"+'(,'-."'/%$01*/%#-23'"4/"$*"#1"''

56"20(##%*$"'7%-%'856"20(##%*$"'9:'

D#$"94$+

;%<($'-."="2'

>#-"$)*"+'7%-%'

>#-"$/$"-"7'7"21$*/0)"'%#7'%?-67*#%&',"%-6$"2''


! "#!

Table 1. The number of responses in each category from the meaning categorisation analysis from the extensive questionnaire survey. The bold numbers show the categories attaining the largest number of statements in total and for each attitude. Descriptive features

Attitudinal features Total + - +&- Blank

Tch 35 4 3 1 43 Sqr 17 4 8 7 36 Sqr&Vqr 3 4 5 14 26 Diverse 6 3 1 8 18 Com 4 5 1 7 17 Vqr 2 1 0 6 9 Tec 0 5 0 1 6 Tch&Com 2 0 0 1 3 Total 69 26 18 45


! "#!

Appendix 1. Questionnaire: Questions in bold are “yes” and “no” questions and the subject could only give a “yes” or “no” answer. The remaining questions are presented as open-ended questions in the questionnaire. 1 Did you change your teaching methods on this occasion?

2 In what way did it change?

3 In what way were you able to interact, by speaking and singing/playing, with the teacher/student?

4 Can you perceive the teacher’s instructions clearly?

5 If “Yes”, describe in your own words the perceived sound.

6 If “No”, describe in your own words the perceived sound.

7 Can you perceive the music examples from the teacher clearly?

8 If “Yes”, describe in your own words the perceived sound.

9 If “No”, describe in your own words the perceived sound.

10 In what way did the communication between you and the teacher/student work?

11 What could further enhance the lesson?

12 Are there any positive aspects with the use of distance learning?

13 Are there any negative aspects with the use of distance learning?

14 What could be improved with the technology based on this lesson, according to your opinion?

15 What could be improved in the teaching, based on this lesson, according to your opinion?


! "#!

Appendix 2. Questions used in the interview guide. Translated from Swedish. Start the interview with questions about name, age, etc.

• How was the lesson/master class?

• On the topic of user freedom o Did you feel limited in your practice?

! Is it the technology? ! Is it the distance between you and the teacher/student? ! Is it the lack of presence by the teacher/student? ! Is it the communication that poses problems?

o If you feel free in your practice ! What is it that makes you feel free in your practice? ! Can you do the things that you want to do? ! To what extent does the feeling of freedom exist?

• Could you complete the master class without being affected by the technology/system

used?

• Did you perceive the technology as a hindrance or as a tool?

• What worked and what didn’t work during the master class? o Technology

! Was it good? ! Was it bad? ! What could be improved? Describe them

o Pedagogically

! Was it good? ! Was it bad? ! What could be improved? Describe them

• How did you perceive the sound quality?

o Can you compare it to a known format/media?

• How did you perceive the sound and video quality?

• If you exclude the video, how did you perceive the sound quality?

• If you exclude the sound, how did you perceive the video quality?

• Did you perceive any “delay/latency” between the sound and video? o If yes, which came first according to you? o Did this delay pose any problem for you?


! "#!

Appendix 3. Table with the equipment for each location. Location Equipment All five locations Tandberg MXP Edge 95 video conferencing systems, 50-52 inch LCD television

screens Piteå 2x Neumann KM184 microphones (occasionally a Microtech Gefell UMT 70s

microphone) 2x Genelec 1030A speakers

Helsinki 2x Neumann TLM-103 microphones 2x Genelec 8030A speakers

Olos 2x Neumann TLM-103 microphones 2x Genelec 8030A speakers

Oulu 2x Neumann TLM-103 microphones 2x Genelec 8030A speakers

Rovaniemi TV speakers 1x Clockaudio limited C600 microphone

Paper 2: Perceived audio quality of realistic FM and DAB+ radio broadcasting systems Submitted to: Journal of the Audio Engineering Society

!"#$

%%!&'($ $ )*+),*-,+.$

$ +$

!!"#!$%!&'()&$*'+)(,$-.'*/'"!(,$0-$#'12'(3&'4567'!"#$%&!"#$%&$'()*+,!"!#$%!&!

'/0$(123+4$562789:;12$(<89/=)4$!/28$':088:0)4$!/28$>:88?123)$/0=$@/0$AB?123+$

$

+!<C1D$#07E12879B$:;$"1F60:C:3B$

)GH1=786$I/=7:$$

!"#$%&'(%!"61$J12F17E1=$/<=7:$K</C79B$:;$/$=7379/C$?2:/=F/89703$8B891L$8<F6$/8$@M(N$=1J10=8$:0$

H6/9$9BJ1$:;$F:=703$/0=$?79$2/918$/21$/JJC71=O$@<1$9:$?/0=H7=96$F:0892/70984$/<=7:$

K</C79B$78$J2:01$9:$?1$70$F:0;C7F9$H796$:9612$812E7F1$=1L/0=8$8<F6$/8$961$0<L?12$:;$

F6/001C8$/0=$961$92/08;12$:;$/0F7CC/2B$=/9/O$5:LJ/21=$9:$@M(N4$81E12/C$:9612$/<=7:$

812E7F18$6/E1$8<J127:2$?79$2/918$96/9$F6/CC1031$961$/<=7:$K</C79B$:;$@M(NO$M0$7=1/C$C1E1C$

:;$K</C79B$78$H6121$0:$J12F17E1=$=7;;1210F1$?19H110$/0$:27370/C$21;1210F1$8730/C$/0=$798$

?2:/=F/891=$F:<0912J/29$78$=191F91=4$8:L197L18$21;1221=$9:$/8$J12F1J9</C$92/08J/210FBO$

"678$J/J12$21E71H8$/0=$7LJC1L1098$K</C79B$F279127/$<81=$;:2$70E18973/9703$961$K</C79B$

/0=$92/08J/210FB$:;$@M(N$F:=1F8$/9$673612$?792/918$/0=$21/C7897F$P>$/0=$@M(N$8B891L8$

70FC<=703$J2:F188:28$/0=$C:H$/0=$6736$?79$2/91$F:=1F8$F:LL:0CB$10F:<09121=$70$

J2:;1887:0/C$/<=7:$?2:/=F/89703O$"H:$C78910703$1QJ127L1098$<8703$961$R"#,I$

21F:LL10=/97:08$(GO+++S$/0=$(GO+.TU$V>#GWIMX$/0=$/$FC:8703$70912E71H$H121$

J12;:2L1=O$R0$961$>#GWIM$91894$/$01H$9BJ1$:;$/0F6:2$8730/C$V:01$H796:<9$C:H,J/88$

;7C912703X$H/8$<81=O$"61$218<C98$86:H1=$96/9$961$F<22109CB$6736189$/E/7C/?C1$8<?F6/001C$

?79$2/91$;:2$@M(N$V+-)$Y?79&8X$H/8$708<;;7F7109$;:2$/99/70703$J12F1J9</C$92/08J/210FB$;:2$

F2797F/C$791L84$?<9$F:LJ/2/?C1$9:$:2$70$8:L1$7089/0F18$?19912$96/0$/$L:=120$P>$8B891LO$

ZQ92/J:C/97:0$:;$=/9/$70=7F/918$96/9$F2797F/C$791L8$L/B$011=$1E10$673612$?79$2/918$9:$?1$

[<=31=$/8$?1703$J12F1J9</CCB$92/08J/2109O$M$C701/2$21C/97:0$?19H110$961$>#GWIM$/0=$

961$(GO+++S$8F/C18$H/8$/C8:$8<331891=O$P2:L$961$70912E71H84$/<=79:2B$;1/9<218$7LJ:29/09$

;:2$961$8<?[1F98\$/88188L109$:;$K</C79B$H121$:?812E1=O$"678$89<=B$F:0FC<=18$96/9$H610$

L/Y703$=1F787:08$:0$?2:/=F/89703$8B891L84$79$78$7LJ:29/09$9:$6/E1$H1CC,;:<0=1=$/0=$

FC1/2CB$=1;701=$F279127/$;:2$/FF1J9/?C1$K</C79B$/0=&:2$92/08J/210FBO$

! "#$%&'()$*&#+];910$?2:/=F/89128$6/E1$61/91=$/0=$<0218:CE1=$=1?/918$/?:<9$961$?189$?79$2/918$

9:$<81$H610$=7379/CCB$?2:/=F/89703$E7/$^1?$2/=7:$/0=$:E12$@_($:2$=7379/C$2/=7:$

/72H/E18O$^610$=18730703$/$=7379/C$812E7F14$96121$/21$9H:$F:092/=7F9703$9/23198$

96/9$86:<C=$;79$H79670$961$=1F7=1=$70E189L109$C1E1CO$]01$9/2319$78$<8703$6736$

10:<36$?79$2/918$8:$J12F17E1=$/<=7:$K</C79B$78$=11L1=$/FF1J9/?C1`$961$:9612$

9/2319$78$961$F/J/F79B$9:$92/08L79$/8$L/0B$F6/001C8$/8$J:887?C1O$"61$9:9/C$F:89$:;$

:H012867J$:;$019H:2Y8$/0=$92/08L799128$:;910$JC/B8$/$=:L70/09$2:C1$;:2$

81C1F97:0$:;$?79$2/9184$C7L79703$961$<JJ12$C1E1C$:;$/<=7:$K</C79BO$5:081K<109CB4$

8927Y703$/0$/JJ2:J27/91$?/C/0F1$?19H110$961$0<L?12$:;$F6/001C8$/0=$96172$/<=7:$

K</C79B$78$/$=1C7F/91$/0=$F2<F7/C$=1F787:0O$R0$?2:/=F/897034$961$21C/97:0867J$

?19H110$J12F17E1=$/<=7:$K</C79B$/0=$?79$2/918$78$F:0970<:<8CB$?1703$1E/C</91=$

/0=$=78F<881=$H79670$/0=$?19H110$2/=7:$/0=$91C1E787:0$F:LJ/0718$/0=$2181/2F6$

?:=718O$R0$8:L1$F/8184$961$81C1F91=$?79$2/918$/21$=1912L701=$87LJCB$?B$[<89$9189703$

96172$7LJ/F9$:0$961$J<?C7F$=<2703$/$21/C$?2:/=F/89`$/C9120/97E1CB4$?79$2/918$/21$

8:L197L18$?/81=$:0$C78910703$9189$218<C98O$"61$/08H128$9:$K<1897:08$/?:<9$H6/9$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )$

/<=7:$K</C79B$78$=18721=$/0=$/FF1J9/?C14$6:H$9678$21C/918$9:$=18F27J9:28$8<F6$/8$

a3::=$10:<36b4$a3::=$b4$/0=$a92/08J/2109b$/0=$H6/9$96181$/F9</CCB$F:2218J:0=$9:$

70$912L8$:;$?79$2/918$/21$F2<F7/C$;:2$L/Y703$/0$70;:2L1=$=1F787:0$/?:<9$961$

21K<721L1098$:;$/$?2:/=F/89703$8B891LO$$

$

@78927?<97:0$:2$1L7887:0$:;$/<=7:$:E12$961$/72H/E18$910=8$9:$/7L$;:2$L707L/C$?79$

2/918$9:$F:J1$H796$961$1E12,L:21$F2:H=1=$/0=$1QJ1087E1$2/=7:$8J1F92<L`$

6:H1E124$1/2C712$C7L79/97:08$;:2$961$L/Q7L<L$J:887?C1$?79$2/918$9:$?1$<81=$70$

=7379/C$812E7F18$E7/$/72H/E18$:2$961$R0912019$9:$961$6:L1$:2$9:$8L/29J6:018$/21$

32/=</CCB$=78/JJ1/2703O$M$2/J7=CB$F6/03703$1E:C<97:0$F/0$?1$8110$9:H/2=8$673612$

J:887?C1$/0=$/;;:2=/?C1$?79$2/918O$"678$F6/0314$:;$F:<2814$H7CC$/CC:H$?2:/=F/89128$

9:$/7L$;:2$/$673612$/<=7:$K</C79B4$:24$/C9120/97E1CB4$9:$L/Y1$L:21$F6/001C8$

J:887?C1$/9$/$37E10$/<=7:$K</C79B$C1E1CO$

$

@<1$9:$?/0=H7=96$C7L79/97:084$961$31012/C$J<?C7F$6/8$?110$;:2F1=$9:$C78910$9:$

K<791$C:H$?79$2/918$8<F6$/8$@M($70$Z<2:J1/0$F:<092718$/0=$>cT$=:H0C:/=8$<81=$

?B$J:29/?C1$=1E7F18O$M8$96181$C7L79/97:08$?1F:L1$L:21$/0=$L:21$:?8:C1914$961$

K<1897:0$:;$H619612$/<=7:$K</C79B$C1E1C8$/21$9::$C:H$;:2$961$=78927?<97:0$/0=$

1L7887:0$:;$@_($/<=7:$/0=$=7379/C$2/=7:$86:<C=$?1$/==21881=O$M$J/29$:;$9678$

=78F<887:0$70FC<=18$1Q/L70703$H619612$79$78$/FF1J9/?C1$96/9$9678$C1E1C$:;$K</C79B$78$

C:H12$96/0$961$K</C79B$:;$:9612$J:J<C/2$F:08<L12$8B891L84$8<F6$/8$5@4$@_@4$(C<,

2/B4$/0=$P>$2/=7:O$R0$/==797:0$9:$J6B87F/C$L1=7/4$=:H0C:/=/?C1$6736,K</C79B$

F:<0912J/298+$70$=7;;12109$;:2L/98$V1O3O4$.O+X$1Q789$/8$H1CC$/8$8921/L703$

/JJC7F/97:08$8<F6$/8$7"<018)4$GJ:97;BT$/0=$:9612$;:2L/98$96/9$/C8:$J2:E7=1$

C78910128$H796$673612$?79$2/91$/<=7:O$$

$

P:2$961$/88188L109$:;$961$J12F17E1=$/<=7:$K</C79B$:;$8B891L8$<8703$J12F1J9</C$

F:=7034$9H:$J21=:L70/09$21F:LL10=/97:08$1Q789d$R"#,I$(GO+++S$(ITU, 1997)$/0=$R"#,I$(GO+.TU4$:;910$21;1221=$9:$/8$>#GWIM (ITU, 2003)O$"6181$6/E1$?110$1LJC:B1=$70$0<L12:<8$91898$/0=$6/E1$J2:=<F1=$E/C</?C1$218<C98O$"61$(GO+++S$78$

=187301=$9:$=1/C$H796$8L/CC$7LJ/72L10984$H6121/8$>#GWIM$78$=187301=$9:$/88188$

70912L1=7/91$K</C79BO$"61$?:2=12$?19H110$96181$K</C79B$2/03184$6:H1E124$78$0:9$

FC1/2CB$=1;701=O$R;$961$K</C79B$8J/0$:;$961$91891=$8B891L8$78$K<791$H7=14$961$<JJ12$

J/29$:;$961$K</C79B$8J/F1$L/B$F/CC$;:2$961$<81$:;$(GO+++S4$H67C1$961$C:H12$J/29$

H:<C=$?1$L:21$8<791=$;:2$/$>#GWIM$9189O$R0$F:0[<0F97:0$H796$8<F6$

F:087=12/97:084$70;:2L/C$=78F<887:08$6/E1$;:F<81=$:0$961$21C/97:0$?19H110$96181$

9H:$8F/C18$/0=$H6/9$9BJ18$:;$=132/=/97:08$8<?[1F98$1QJ12710F1$=<2703$8<F6$91898O$

$

"678$J/J12$70E18973/918$961$J12F17E1=$/<=7:$K</C79B$:;$P>$/0=$@M(N$8B891L8$/9$

=7;;12109$?79$2/918$/0=$H619612$961$K</C79B$J2:E7=1=$78$8<;;7F7109O$"678$89<=B$6/8$

;7E1$:?[1F97E18d$7X$9:$/88188$961$673612$?79$2/918$70$@M(N$/8$H1CC$/8$21/C7897F$P>$

/0=$C:H$/0=$6736$?79$2/91$@M(N$8B891L8$?B$L1/08$:;$=7;;12109$=1;70797:08$:;$

/<=7:$K</C79B`$77X$9:$F:LJ/21$96181$P>$/0=$@M(N$8B891L8$H796$:01$/0:9612`$777X$

9:$70E18973/91$H6/9$/==797:0/C$70;:2L/97:0$/?:<9$961$J12F17E1=$/<=7:$K</C79B$:;$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

+$!"#"$%%%"&'()*+,-"+./01$%%%")'2."+./1$%%%"%2/3/4-2+"-5$)$HHHO/JJC1OF:L&79<018&$

T$HHHO8J:97;BOF:L&$

!"#$

%%!&'($ $ )*+),*-,+.$

$ T$

8<F6$8B891L8$F/0$?1$1C7F791=$?B$F:0=<F9703$70912E71H8$/;912$C78910703$91898`$7EX$9:$

L/Y1$:?812E/97:08$:0$961$21C/97:0$?19H110$>#GWIM$/0=$(GO+++S$1E/C</97:0$

8F/C18`$/0=$EX$9:$1897L/91$H6/9$?79$2/918$/21$C7Y1CB$9:$?1$21K<721=$;:2$

92/08J/210FBO$"61$912L$a21/C7897F$8B891Lb$6121$21;128$9:$961$F6/70$:;$J2:F188:28$

/0=$F:=1F8$96/9$H:<C=$?1$F:LL:0CB$10F:<09121=$70$J2:;1887:0/C$/<=7:$

?2:/=F/89703O$"61$8B891L8$F:0927?<91$9:$961$1F:C:37F/C$E/C7=79B$:;$961$218<C98$:;$

961$<JF:L703$1QJ127L1098O$

, -('*&+.(/0*$1+*#+2%&/')/3$*#4+313$563+M$?2:/=F/89703$8B891L$31012/CCB$F:087898$:;$F72F<798$;:2$F:0927?<97:04$

=78927?<97:04$/0=$1L7887:0O$"6181$21;12$9:$=7;;12109$J/298$:;$961$8B891Ld$

F:0927?<97:0$e$961$019H:2Y$?19H110$J2:=<F97:0$87918`$=78927?<97:0$e$961$019H:2Y$

=1C7E12703$961$J2:32/LL1$9:$961$92/08L79912`$/0=$1L7887:0$e$961$IP$8730/CO$$

$

^610$=78F<88703$/<=7:$K</C79B4$81E12/C$J:887?C1$K</C79B$F279127/$/21$<81=4$

18J1F7/CCB$961$F:0F1J9$:;$a92/08J/210FBb4$H67F6$F/0$?1$70912J2191=$70$81E12/C$

H/B8O$]01$F:LL:0$<81$78$a?79$92/08J/2109bO$(79$92/08J/2109$21;128$9:$H610$/$c5>$

8/LJC1$J/8818$962:<36$8:L1$/JJ/2/9<8$<0/;;1F91=$H796$7=1097F/C$F:09109$:0$

70J<9$/0=$:<9J<9$?79,?B,?79O$M$81F:0=$=1;70797:0$L7369$?1$F/CC1=$aJ12F1J9</C$

92/08J/210FBbO$c12F1J9</C$92/08J/210FB$L1/08$J1:JC1$F/00:9$J12F17E1$F6/0318$

70$/<=7:$K</C79B$H610$F:LJ/2703$/<=7:$9:$961$:27370/C$V?1;:21$10F:=703$:2$

J2:F188703XO$$"678$L1/08$96/9$70$/$C78910703$9189$961$8F:21$:;$/$J12F1J9</CCB$

92/08J/2109$791L$86:<C=$0:9$86:H$/0B$89/97897F/CCB$87307;7F/09$=7;;1210F1$;2:L$961$

21;1210F1$8730/CO$M$9672=$0:97:04$a/FF1J9/?C1b$?2:/=F/89$/<=7:$K</C79B4$21K<7218$/$

?19912$8F:21$;:2$/CC$791L8$96/0$:01$32/=1$=:H0$:0$961$.,32/=1$1E/C</97:0$8F/C1$

<81=$70$R"#,I$(GO+++S4$7O1O4$/$G<?[1F97E1$@7;;1210F1$f2/=1$VG@fX$g$,+O$"678$C7L79$78$

961$C:H189$/CC:H1=$218<C9$:;$/$C78910703$9189$;:2$J2:=<F97:0$:2$F:0927?<97:0$

F72F<798O$R0$Z(#$"1F6$TTT-4$9678$70912J219/97:0$78$=18F27?1=$70$961$;:CC:H703$H/Bd$

$

R;$F/8F/=1=$F:=1F$F6/708$/21$9:$?1$F:087=121=$;:2$?2:/=F/89$<81$9610$961$

K</C79B$F279127:0$86:<C=$?1$96/9$0:01$:;$L/9127/C$86:<C=$J2:=<F1$/0$

/E12/31$=7;;,32/=1$H:281$96/0$,+O*$Vh!"#$"!%&'(")'*%)+,%)-++,.&+/hXO$R;$/CC$961$91891=$791L8$8F:21$?19912$96/0$,+O*4$9610$H1$F/0$F:087=12$961$F6/70\8$

J12;:2L/0F1$9:$?1$/FF1J9/?C1O$W:H1E124$7;$961$/E12/31$:E12$/CC$791L8$78$

?19912$96/0$e+O*4$?<9$8:L1$9189$791L8$8F:21$H:281$96/0$96784$9610$H1$L<89$

?1$H/2B$:;$<8703$8<F6$/$F6/70O$R;$961$/E12/31$:E12$/CC$791L8$78$H:281$96/0$,

+O*$9610$961$F6/70$86:<C=$0:9$?1$F:087=121=$/FF1J9/?C1$;:2$?2:/=F/89$<81O (EBU, 2010)$

$

M8$9678$K<:9/97:0$8<3318984$1E10$H610$/$8J1F7;7F$F:=1F$86:H8$/0$:E12/CC$

/FF1J9/?C1$J12;:2L/0F1$/E12/31=$/F2:88$791L84$F129/70$8703C1$791L8$L/B$897CC$

86:H$/0$<0/FF1J9/?C1$K</C79B4$/$F:0F120$96/9$/0B$/0/CB878$86:<C=$F:087=12O$

G7L7C/2CB4$Z(#$(cA$*+-$(EBU, 1999)$<818$961$J62/81$a70=789703<786/?C1$K</C79Bb$/0=$Z(#$(cA$*-U$(EBU, 2009)$<818$961$J62/81$a?2:/=F/89$K</C79Bb$9:$=1;701$/FF1J9/?C1$K</C79BO$"61$C/9912$0:97:0$811L8$9:$?1$7LJC7F79CB$=1;701=$/8$1QF1CC109$

K</C79B$:2$/$8F:21$g$i*$:0$961$>#GIWM$8F/C1$70$Z(#$(cA$*-U4$G1F97:0$-O+O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ U$

R0$/==797:04$96121$78$/$J:887?C1$;:<296$/<=7:$K</C79B$F279127:04$aP>$K</C79BbO$M8$P>$

6/8$?110$961$J21=:L70/09$H/B$:;$/<=7:$?2:/=F/897034$79$78$/$=1,;/F9:$21;1210F1$

J:7094$:2$/$8:29$:;$/0F6:2$96/9$C/912$8B891L8$F/0$/0=$H7CC$?1$F:LJ/21=$H796$?B$

C78910128O$5:081K<109CB4$/$F:LJ/278:0$?19H110$@M(N$/0=$P>$78$:;$70912189O$G<F6$

/$F:LJ/278:0$H:<C=$0:9$?1$892/7369;:2H/2=$/8$961$8B891L8$<0=12$9189$H7CC$?1$

8<8F1J97?C1$9:$=7;;12109$;:2L8$:;$K</C79B$=132/=/97:0$<0=12$21/C,C7;1$F:0=797:08O$

W:H1E124$7;$F:0=797:08$/21$8J1F7;71=4$/$F:LJ/278:0$78$J:887?C1O$

$ $

M<=7:$:E12$Rc$F:0927?<97:0$F72F<798$21K<721$673612$?79$2/918$/0=$96<8$?/0=H7=968$

96/0$96:81$<81=$70$1L7887:0$;:2$=78927?<97:0$8B891L8O$"678$78$/$F:081K<10F1$:;$

961$;/F9$96/9$/$87307;7F/09$0<L?12$:;$891J8$:;$21,10F:=703$L<89$?1$L/=1$70$961$

F:0927?<97:0$F6/704$1O3O4$70$1=79703$:2$:9612$L/07J<C/97:0$:;$961$J2:32/LL1O$M8$/$

218<C94$F/8F/=703$/291;/F98$/21$/==1=$9:$961$;70/C$J2:32/LL1$?1;:21$=78927?<97:0O$

":$/E:7=$8<F6$/291;/F984$961$?79$2/91$6/8$9:$?1$70F21/81=$(Geiger, o.a., 2007) (EBU, 2005)$(EBU, 2007) (EBU, 2010)O$MC96:<36$9678$;/F9$6/8$?110$21J:291=4$79$78$0:9$H7=1CB$Y0:H0$8:$79$6/8$0:9$70;C<10F1=$961$2:CC$:<9$:;$?79$2/91$F:LJ21881=$/<=7:O$

>:21$=78F<887:04$2181/2F64$/0=$C78910703$91898$:0$961$F/8F/=703$J12;:2L/0F1$:;4$

;:2$7089/0F14$MM5$/21$011=1=$9:$<0=1289/0=$961$J610:L10:0$1E10$?19912O$$

$

"61$F:89$;:2$Rc$019H:2Y8$<81=$;:2$6736$?79$2/918$78$8C:HCB$=1F21/8703O$^796$C:H12$

J27F18$;:2$673612$?/0=H7=968$F:L7034$9678$/F9</CCB$/CC:H8$961$<81$:;$8<;;7F7109CB$

6736$?79$2/918$9:$/E:7=$961$F/8F/=703$/291;/F98O$P73<21$+$86:H8$961$1E:C<97:0$:E12$

97L1$:;$/E/7C/?C1$?/0=H7=968$(Gross, 2011)O$":=/B4$96121$78$0:$21/8:0$9:$Y11J$961$E12B$C:H$?79$2/918$96/9$89/291=$9:$?1$<81=$H610$/<=7:$:E12$Rc$F:0927?<97:0$

8B891L8$H121$7092:=<F1=$[<89$/;912$)***O$"6121$78$/$F:0970<:<8$70F21/81$70$961$

/F9</C$?79$2/918$:E12$/CC$Rc$019H:2Y8$/9$/$L:21$:2$C188$<0F6/031=$J27F1O$))

$

$

)*+,&-!./!0123,%*24!25!'1'*3'#3-!#'467*6%8!52&!#'(9:3'4-!;*/-/<!*4!$7*%(8-$=<!0%8-&4-%!8'&67'&-<!'46!%8-!>4%-&4-%!(Gross, 2011)/!

!"#$

%%!&'($ $ )*+),*-,+.$

$ .$

$

ZQ/LJC18$:;$=7;;12109$8B891L8$/0=$961$/88:F7/91=$F:=1F8$/0=$9BJ7F/C$?79$2/918$/21$

;:<0=$70$"/?C1$+O$5C1/2CB4$9:=/B$9BJ7F/C$@_($/0=$F:08<L12$8B891L8$<81$673612$?79$

2/918$96/0$=7379/C$2/=7:$?2:/=F/89703$8B891L8O$$

)?'#3-!./!0@'A:3-$!25!(26-($!'46!%B:*('3!#*%!&'%-$!,$-6!*4!(,&&-4%!(24$,A-&!$B$%-A$!'46!*4!C"D!'46!C"DE/!

FB$%-A! 0@'A:3-$!25!(26-($! ?B:*('33B!,$-6!#*%!&'%-$!;9#*%G$=!C"DE! MM5&WZ,MM5$ T),+)i$$

C"D! >cZf,+&)$!/B12$RR$ SU,+-)$$

CHD! >cZf$!/B12$RR$

@:C?B$@7379/C$

+-),).S$$V89121:Xjj$

UUi$$V89121:$N$L<C97F6/001CX$

D3,I&'B!6*$($! c5>j&!:88C188$F:=703$ kS$>?79&8$V89121:$N$L<C97F6/001CX$

CHC! c5>j&@"G&@:C?B$@7379/C$ )T*U$$V89121:X4$SU*$,+.**$

VL<C97F6/001CX$

J43*4-!A,$*(!('%'32+,-$! P!M5j$ ki**$$V89121:X$

K-#!$%&-'A*4+! >cZf,+$!/B12$RRR&^70=:H8$

>1=7/$M<=7:&MM5$

T),T)*$4$+)i$$9BJ7F/C$

*?,4-$! MM5$ +)i,).S$$

F:2%*5B! ]33$_:2?78$ -S,T)*$$

K*A:! MM5&WZ,MM5$ SU,).S$$

j$c5>$/0=$P!M5$L1/0$96/9$0:$C:88B$=/9/$F:LJ21887:0$6/8$?110$<81=O$

jj$(79$2/918$<81=$70$GH1=10O$

$

$

M$L:21$=19/7C1=$C::Y$/9$9BJ7F/C$?79$2/918$70$@M(N$F/0$?1$;:<0=$70$P73<21$)O$

$

$

$

)*+,&-!L/!?8-!$,A!25!2((,&&-4(-$!25!6*55-&-4%!#*%!&'%-$!*4!C"DE!*4!%8-!(2,4%&*-$!",$%&'3*'<!M-&A'4B!'46!F7*%N-&3'46!*4!O'B!LP.L!Q8%%:RGGK2842&%/2&+GC"DG*46-@/8%A3S/!

5C1/2CB4$81E12/C$J:887?C1$=1;70797:08$:;$/<=7:$K</C79B$:;$/$8B891L$1Q789$/0=$/$H7=1$

8J/0$:;$?79$2/9184$L/0B$:;$961L$C:H12$96/0$H6/9$78$1LJC:B1=$?B$:9612$/<=7:$

/JJC7F/97:084$/21$<81=$;:2$@M(NO$"61$9:9/C$8730/C$J/96$:;$=7379/C$?2:/=F/89703$

!"#$

%%!&'($ $ )*+),*-,+.$

$ S$

70FC<=18$L<C97JC1$/<=7:$F:=703$/0=$=1F:=703$J:7098$H6121$8730/C$=132/=/97:08$

:FF<2O$":$6/0=C1$96181$788<18$;:2$;<9<21$/<=7:$?2:/=F/897034$79$H7CC$?1$7LJ:29/09$

9:$<0=1289/0=$H67F6$K</C79B$F279127/$/21$8<79/?C1$/0=$6:H$96181$86:<C=$?1$

70912J2191=$/0=$7LJC1L1091=O$

7 8*3$5#*#4+$53$3+9+)&'*#4+:%&)53353++R0$961$;:CC:H703$1QJ127L10984$/$0<L?12$:;$F:=703$J2:F18818$J:887?C1$;:2$

?2:/=F/89703$J<2J:818$H7CC$?1$70E18973/91=$;:2$96172$J12F17E1=$/<=7:$K</C79BO$R0$

9678$81F97:04$961$=7;;12109$J2:F18818$H7CC$?1$=18F27?1=O$"H:$9189$L196:=:C:3718$

H121$<81=$70$96181$1QJ127L10984$H67F6$H121$=7E7=1=$709:$"189$+$/0=$"189$)O$"189$

+$<81=$(GO+++S$(ITU, 1997)$/0=$"189$)$<81=$>#GWIM$(ITU, 2003)O$P<29612$=19/7C8$:0$961$1QJ127L1098$F/0$?1$;:<0=$70$G1F97:0$UO$!:<=0188$C1E1C8$70$!#PG$21;12$9:$

21F:LL10=/97:0$Z(#$I+)i$(EBU, 2010)O$$

7;! <53$+!++]01$:?[1F97E1$:;$9678$9189$H/8$9:$/88188$961$673612$?79$2/918$70$@M(N$?B$L1/08$:;$

=7;;12109$=1;70797:08$:;$/<=7:$K</C79BO$M0:9612$:?[1F97E1$H/8$9:$L/Y1$

:?812E/97:08$:0$961$21C/97:0$?19H110$961$8F/C18$:;$961$>#GWIM$/0=$(GO+++S$

1E/C</97:0$L196:=8$?B$F:LJ/2703$218<C98$;2:L$9678$C78910703$91894$H67F6$<81=$961$

(GO+++S$L196:=$H796$218<C98$;2:L$961$Z(#$@&@M(M$J2:[1F9$21J:29$(cA$*-U$

(EBU, 2009)$;:2$H67F6$961$>#GWIM$L196:=$H/8$<81=O$P:2$9678$21/8:04$961$8/L1$10F:=12$/0=$=1F:=12$/0=$961$8/L1$81997038$<81=$;:2$961$9189$=18F27?1=$70$(cA$

*-U$H121$/C8:$<81=$;:2$9678$9189O$

$

MCC$/<=7:$81C1F91=$;:2$961$9189$H/8$10F:=1=$709:$961$8J1F7;7F$9BJ1$:;$WZ$MM5$<81=$

70$@M(N$<8703$/$F:LL/0=$C701$8:;9H/21$10F:=12$F/CC1=$a918910Fb$E1287:0$+O)O*$

V?<7C9$'<CB$+S4$)**lX$;2:L$@:C?B$/0=$9610$=1F:=1=$<8703$961$@:C?B$F:LL/0=$C701$

=1F:=12$a9189=1Fb$E1287:0$+O*O*$V?<7C=$T+$>/B$)**lXO$$

$

R0$@M(N4$961$?79$2/91$;:2$/$8<?F6/001C$78$0:9$:0CB$<81=$;:2$/<=7:$=/9/$?<9$/C8:$;:2$

J2:32/LL1$/88:F7/91=$=/9/$VcM@X4$122:2$F:221F97:04$19FO$"61$?79$2/91$/E/7C/?C1$

;:2$/<=7:$=/9/$78$/?:<9$ii,-+m$:;$961$8<?F6/001C$?79$2/91$V=<1$9:$122:2$

F:221F97:0X$L70<8$961$?79$2/91$<81=$;:2$cM@ (ETSI, 2010)O$"61$6736189$8<?F6/001C$?79$2/91$/CC:H1=$70$@M(N$78$+-)$Y?79&8$/0=$9:$Y11J$961$0<L?12$:;$=7;;12109$?79$

2/918$/9$/$C1E1C$96/9$H:<C=$/E:7=$/$9::$C10396B$9189$/0=$J:887?C1$C7891012$;/973<14$

;:<2$?79$2/918$8J/F1=$T)$Y?79&8$/J/29$H121$F6:810O$5:081K<109CB4$961$8<?F6/001C$

?79$2/918$<81=$?B$961$10F:=12$H/8$-S$Y?79&84$+)i$Y?79&84$+S*$Y?79&84$/0=$+-)$

Y?79&8O$GJ1F92/C$?/0=$21JC7F/97:0$VG(IX$H/8$:0CB$<81=$;:2$961$C:H189$?79$2/91O$$

$

"61$?79$2/918$;:2$cM@$H121$+OTT$Y?79&8$;:2$961$C:H189$?79$2/91$H796$G(I$/0=$)O.T$

Y?79&8$;:2$961$96211$673612$?79$2/918$H796:<9$G(IO$"6181$E/C<18$H121$F6:810$9:$?1$

961$8/L1$/8$70$961$C78910703$9189$21J:291=$70$Z(#$(cA$*-U$(EBU, 2009)O$"6121$/214$6:H1E124$/$0<L?12$:;$812E7F18$;:2$H67F6$673612$cM@$?79$2/918$J2:?/?CB$

H:<C=$?1$81C1F91=$/0=$961$L/Q7L<L$?79$2/91$;:2$cM@$F/0$?1$819$/8$6736$/8$l-$

Y?79&8$;:2$Ui$YWn$8/LJC1$2/91$H610$G(I$78$=78/?C1=O$R;$/$673612$cM@$?79$2/91$78$

81C1F91=$/0=$7;$961$8<?F6/001C$?79$2/91$21L/708$961$8/L14$961$/<=7:$K</C79B$H7CC$

=1F21/81O$"61$/<=7:$?79$2/918$70FC<=703$cM@$H121$ilO)$Y?79&84$++.Oi$Y?79&84$+U.O+$

!"#$

%%!&'($ $ )*+),*-,+.$

$ l$

Y?79&84$/0=$+lUO.$Y?79&8O$P:2$L:21$=19/7C8$:;$961$cM@$7081297:04$811$G1F97:0$l$70$

Z(#$(cA$*-UO$"61$8/LJC703$;21K<10FB$H/8$/CH/B8$819$9:$Ui$YWn$/0=$/CC$/<=7:$

H/8$10F:=1=$70$89121:O$G11$"/?C1$)$;:2$0/L18$:;$961$81997038$/0=$/0$:E12E71H$:;$

961$?79$2/918O$

$

?'#3-!L/!T26-(!$-%%*4+$!*4!?-$%!./!

U'A-! F,#(8'44-3!#*%!&'%-!Q9#*%G$S!

",6*2!#*%!&'%-!;*4(3,6*4+!V"C=!Q9#*%G$S!

V"C!#*%!&'%-!Q9#*%G$S!

F:-(%&'3!D'46!W-:3*('%*24!;FDW=!

V'&'A-%&*(!F%-&-2!;VF=!

C"DE!XY!;FDW=! $$-S$ $$ilO)$ +OTT$ o18$ A:$

C"DE!.LZ! +)i$ ++.Oi$ )O.T$ A:$ A:$

C"DE!.YP! +S*$ +U.O+$ )O.T$ A:$ A:$

C"DE!.XL! +-)$ +lUO.$ )O.T$ A:$ A:$

$

$

]01$1QF12J9$70$9678$C78910703$9189$VGJ11F6$VJ/0XX$H/8$10F:=1=$/0=$=1F:=1=$70$

9H:$E1287:08O$"61$;7289$E1287:0$H/8$961$:27370/C$8J11F6$8730/C$/0=$961$81F:0=$

E1287:0$H/8$961$8/L1$/<=7:$8109$962:<36$/0$>cZf,+$M<=7:$!/B12$RR$10F:=12$/9$

TiU$Y?79&84$89121:4$Ui$YWn4$/0=$/$=1F:=12$;7E1$97L18O$"61$10F:=12$/0=$=1F:=12$

H/8$/$8J1F7/C$E1287:0$:;$MH/E1$M<=7:$?B$P>'$G:;9H/21$F/CC1=$MH/E1$GI$E1287:0$

)OT4$H67F6$709120/CCB$<818$"::!/L1O$"61$/<=7:$?/0=H7=96$:;$9678$10F:=12$/9$9678$

?79$2/91$1QF11=1=$)*$YWnO$"61$J2:=<F97:0$/9$?2:/=F/89703$F:LJ/0718$:;910$

70FC<=18$/$0<L?12$:;$F/8F/=184$1O3O4$;:2$GH1=786$I/=7:$9678$78$E/C7=$;:2$>cZf,+$

M<=7:$!/B12$RRO$W10F14$9:$70E18973/91$798$70;C<10F1$:0$961$/<=7:$K</C79B$961$

F/8F/=1$H/8$70FC<=1=O$

$

Z/F6$791L$9:$?1$<81=$70$961$C78910703$9189$H/8$;70/CCB$/=[<891=$9:$,)T$!#PG$/0=$

H/8$/C8:$F/21;<CCB$8B0F62:07n1=$H796$:9612$791L8$:27370/9703$;2:L$961$8/L1$

1QF12J9O$G11$P73<21$T$;:2$/$87LJC7;71=$32/J6$:;$"189$+O$

$

$

$

)*+,&-![/!D32(9!6*'+&'A!25!?-$%!./!

7;, <53$+,+"189$)$/881881=$21/C7897F$P>$/0=$@M(N$8B891L8$<8703$=7;;12109$=1;70797:08$:;$

/<=7:$K</C79B$/0=$F:LJ/21=$P>$/0=$@M(N$8B891L8O$M8$9678$78$F:LL:0$J2/F97F1$70$

9:=/B\8$?2:/=F/897034$P>$/0=$@M(N$70$9678$1QJ127L109$?:96$<81$:01$?/0=$/0=$

L<C97?/0=$/<=7:$J2:F188703$9:$21=<F1$<07091097:0/C$C1E1C$=7;;1210F184$9:$/=/J9$

961$=B0/L7F8$:;$961$/<=7:$F:09109$;:2$961$C78910703$10E72:0L1098$F:LL:0$9:$

C789101284$9:$21=<F1$961$;21K<10FB$218J:081$=7;;1210F18$?19H110$=7;;12109$/<=7:$

F:091094$/0=4$70$8:L1$F/8184$9:$F21/91$/$F129/70$a8:<0=b$;:2$/$J/297F<C/2$F6/001CO$

R0$P>4$:01$?/0=$V8:L197L18$21;1221=$9:$/8$H7=1,?/0=X$/0=$L<C97?/0=$J2:F188703$

78$/C8:$<8</CCB$FC:81CB$/=/J91=$9:$961$J21,1LJ6/878$C7L79703$01F188/2B$;:2$961$P>$

89121:$8B891LO$":$F21/91$21/C7897F$?2:/=F/89$8B891L84$79$H/8$=1F7=1=$9:$70FC<=1$

:01$?/0=$/0=$L<C97?/0=$/<=7:$J2:F188703$70$961$@M(N$/0=$P>$8B891L8$9:$?1$

91891=O$$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ i$

G70F1$=7;;12109$/<=7:$F:09109$21K<7218$=7;;12109$9BJ18$:;$/<=7:$J2:F1887034$9H:$

J2:F188703$F/913:2718$H121$F21/91=O$]01$F/913:2B4$c)4$F:2218J:0=1=$9:$961$2/=7:$

F6/001C$GH1=786$I/=7:$c)4$H67F6$?2:/=F/898$FC/887F/C$/0=$F:091LJ:2/2B$L<87F$

/8$H1CC$/8$[/nn$/0=$;:CY$L<87FO$"61$:9612$F/913:2B4$cT4$F:2218J:0=1=$9:$961$2/=7:$

F6/001C$GH1=786$I/=7:$cT4$H67F6$?2:/=F/898$J:J$L<87F4$01H84$/0=$F<C9<2/C$/0=$

8:F7/C$J2:32/LL18O$"61$J2:F188703$;:2$961$c)$F/913:2B$H/8$=1F7=1=$9:$70FC<=1$

:0CB$:01$?/0=$/<=7:$J2:F188703$/0=$0:$L<C97?/0=$/<=7:$J2:F188703$/0=$961$

J2:F188703$;:2$961$cT$F/913:2B$H/8$=1F7=1=$9:$70FC<=1$:01$?/0=$J2:F188703$

;:CC:H1=$?B$L<C97?/0=$/<=7:$J2:F188703O$$

$

":$F:LJ/21$961$/<=7:$K</C79B$:;$P>$/0=$@M(N$:0$961$8/L1$912L84$79$H/8$

7LJ:29/09$;:2$961$/<=7:$J2:F188703$9:$?1$961$8/L1$;:2$?:96$8B891L84$H796$961$

1QF1J97:0$:;$961$J21,1LJ6/878$C7L79703$;:2$P>O$":$108<21$9678$F:0878910FB4$/<=7:$

F:09109$96/9$C/912$L7369$?1$81C1F91=$;:2$961$C78910703$9189$H/8$8:291=$709:$961$9H:$

F/913:27184$c)$/0=$cTO$"61$9:9/C$/L:<09$:;$/<=7:$70$961$9H:$F/913:2718$H/8$/?:<9$

81E10$6:<28$/0=$H/8$C/912$21=<F1=$9:$/$;1H$1QF12J98O$"61$C:<=0188$:;$1/F6$/<=7:$

791L$70$?:96$F/913:2718$H/8$;7289$0:2L/C7n1=$9:$,)T$!#PGO$"610$/<=7:$70$961$c)$

F/913:2B$H/8$J2:F1881=$<8703$961$/=E/0F1=$:01$?/0=$/<=7:$J2:F188:2$P/F9<L$

5/=10n/U$70$8<F6$/$H/B$96/9$961$218<C9703$C:<=0188$;:2$1E12B96703$70$961$F/913:2B$

/8$/$H6:C1$21/F61=$,)T$!#PGO$#8703$9678$892/913B$L1/09$96/9$961$:E12/CC$C:<=0188$

F:2218J:0=1=$9:$961$21F:LL10=1=$C:<=0188$/FF:2=703$9:$Z(#$"1F6$TTUU (EBU, 2011)O$"61$J21819$<81=4$c21819$c)4$H/8$/$8C7369$L:=7;7F/97:0$:;$961$J21819$F<22109CB$<81=$?B$961$GH1=786$I/=7:$c)$F6/001C$=<2703$1E107038O$c21819$c)$

J12;:2L1=$/$L707L<L$:;$86:29,912L$C7L79703$/0=$L/70CB$2/781=$961$C1E1C$:;$8:;9$

J/88/318$70$961$/<=7:O$$

$

"61$/<=7:$70$961$cT$F/913:2B$H/8$J2:F1881=$<8703$961$8/L1$:01$?/0=$/<=7:$

J2:F188:2$;:CC:H1=$?B$961$8:;9H/21$L<C97?/0=$/<=7:$J2:F188:2$(21/Y/H/B.O$"61$

J21819$<81=$;:2$961$:01$?/0=$J2:F188:2$70$9678$F/913:2B4$c21819$cT4$H/8$/$8C7369$

L:=7;7F/97:0$:;$961$J21819$F<22109CB$<81=$?B$961$GH1=786$I/=7:$c)$F6/001C$

=<2703$=/B97L1O$"678$J21819$/C8:$=7=$/$L707L<L$:;$86:29,912L$C7L79703$/0=$

L/70CB$2/781=$961$C1E1C$:;$8:;9$J/88/318$70$961$/<=7:4$?<9$L:21$8:$96/0$961$c21819$

c)O$"61$L<C97?/0=$J2:F188:2$<81=$/$3109C1$J21819$96/9$=7=$8:L1$=B0/L7F$

F:LJ21887:0$H796$/$L:=12/91$F:LJ21887:0$2/97:$70$87Q$;21K<10FB$?/0=8O$"61$

=27E1$709:$798$;70/C$C7L7912$H/8$819$70$8<F6$/$H/B$96/9$961$218<C9703$C:<=0188$;:2$

1E12B96703$70$961$F/913:2B$/8$/$H6:C1$21/F61=$,)T$!#PGO$$

$

"61$J2:F1881=$/<=7:$;:2$961$c)$/0=$961$cT$F/913:2718$H/8$;1=$709:$/$@M(N$

10F:=12$/0=$/C8:$709:$9H:$=7;;12109$P>$8B891L8$70$H67F6$961$?2:/=F/89$

J2:F188:28$:0CB$=7=$J21,1LJ6/878$FC7JJ703$/0=$0:$:9612$/<=7:$J2:F188703O$"61$

=27E18$709:$961$J21,1LJ6/878$FC7JJ128$H121$819$8:$96/9$961$>cp$c:H12$/9$8:L1$

J:709$21/F61=$?<9$01E12$1QF11=1=$961$>cp$c:H12$C7L79$=1;701=$70$R"#,I$(GOU+)$

(ITU, 1998)O$R0$9678$H/B4$/CC$961$8B891L8$<81=$/<=7:$J2:F188703$96/9$78$21/C7897F$/0=$0:2L/CCB$:FF<22703$70$?2:/=F/89703O$"61$21;1210F1$8730/C8$H121$1Q92/F91=$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

U$HHHO;/F9<LO81$

.$HHHOFC/188:01=H/2=8OF:L$

!"#$

%%!&'($ $ )*+),*-,+.$

$ -$

;2:L$961$:<9J<9$:;$961$/<=7:$J2:F188:28$VL<C97?/0=&:01$?/0=XO$G11$P73<21$U$;:2$

/$?C:FY$=7/32/L$:;$"189$)O$

$

$)*+,&-!\/!D32(9!6*'+&'A!25!?-$%!L/!

7;,;! =->?+f7E10$961$J218109$97L1$218927F97:084$:0CB$/$8<?819$:;$961$81E10$6:<28$:;$J2:F1881=$

/<=7:$H/8$;1=$709:$/0$/F9</C$@M(N$10F:=12O$MCC$81E10$6:<28$:;$/<=7:$H121$

10F:=1=$709:$WZ$MM5$<8703$/$F:LL/0=$C701$10F:=12$;2:L$@:C?B$/0=$=1F:=1=$

?/FY$9:$c5>O$"678$9BJ1$:;$WZ$MM54$6:H1E124$H/8$0:9$1Q/F9CB$961$:01$<81=$70$961$

@M(N$8B891L4$?<9$86:<C=$/JJ2:Q7L/91$961$F:221F9$9BJ1$H1CCO$"61$?79$2/918$<81=$

?B$961$F:LL/0=$C701$10F:=12$H121$819$9:$961$/F9</C$/<=7:$?79$2/918$<81=$?B$961$

@M(N$10F:=124$7O1O4$?798$<81=$70$@M(N$;:2$122:2$F:221F97:0$/0=$J2:32/LL1$

/88:F7/91=$=/9/$VcM@X$H121$9/Y10$709:$/FF:<09O$$

$

"61$81C1F91=$1QF12J98$;:2$961$C78910703$9189$H121$;1=$709:$961$@M(N$10F:=12$

P/F9<L$>Mc).*Z$/0=$961$/<=7:$;2:L$961$L:079:2$:<9J<9$H/8$21F:2=1=O$"61$

/<=7:$H/8$/C8:$8109$:E12$961$/72$87L<C9/01:<8CB$962:<36$/$@M(N$L<C97JC1Q$/0=$/$

92/08L79912$JC/F1=$70$961$A/FY/$2137:0$:;$G9:FY6:CL4$/0=$961$/<=7:$;2:L$/$@M(N$

21F17E12$H/8$21F:2=1=O$M8$0:$=7;;1210F18$F:<C=$?1$;:<0=$?19H110$961$/<=7:$;2:L$

961$L:079:2$:<9J<9$/0=$961$21F17E124$961$/<=7:$;2:L$961$L:079:2$:<9J<9$H/8$

F6:810$;:2$961$C78910703$9189O$$

$

"61$8<?F6/001C$?79$2/918$<81=$?B$961$@M(N$10F:=12$H121$Ui$Y?79&84$SU$Y?79&84$-S$

Y?79&84$+)i$Y?79&84$+S*$Y?79&84$/0=$+-)$Y?79&8O$GJ1F92/C$?/0=$21JC7F/97:0$VG(IX$

H/8$:0CB$<81=$;:2$961$96211$C:H12$?79$2/9184$/0=$J/2/L1927F$89121:$VcGX$H/8$:0CB$

<81=$;:2$961$C:H189$?79$2/91O$"61$?79$2/918$;:2$J2:32/LL1,/88:F7/91=$=/9/$VcM@X$

H121$+OT$Y?79&8$;:2$961$96211$C:H12$8<?F6/001C$?79$2/918$/0=$)Ol$Y?79&8$;:2$961$

96211$673612$8<?F6/001C$?79$2/918O$"6181$E/C<18$H121$F6:810$9:$?1$87L7C/2$9:$

96:81$<81=$70$Z(#$(cA$*-U$(EBU, 2009)$/0=$70$"189$+$70$9678$J/J12O$"61$/<=7:$?79$2/918$70FC<=703$cM@$H121$UTO)$Y?79&84$.lO-$Y?79&84$ilO)$Y?79&84$++.Oi$Y?79&84$+U.O+$

Y?79&84$/0=$+lUO.$Y?79&8O$"61$8/LJC1$2/91$H/8$/CH/B8$Ui$YWn$/0=$/CC$/<=7:$H/8$

10F:=1=$70$89121:O$$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +*$

$

Z/F6$791L$9:$?1$<81=$70$961$C78910703$9189$H/8$;70/CCB$/=[<891=$9:$,)-$!#PG4$0:9$,)T$

!#PG$/8$70$"189$+O$"678$/=[<89L109$J21E1091=$:E1286::98$218<C9703$;2:L$961$

/<=7:$F:=1F$;2:L$?1703$FC7JJ1=O$R0$"189$)4$:01$:;$961$910$1QF12J98$H/8$/$))$=($

/9910</91=$F:JB$:;$:01$:;$961$:9612$0701$1QF12J98O$G:L1$:;$961$791L8$218<C9703$

;2:L$961$/9910</91=$E1287:0$H:<C=$?1$FC7JJ1=$70$961$0:2L/C78/97:0$7;$961B$H121$

0:2L/C781=$9:$,)T$!#PG$/8$70$"189$+O$W10F1$961$C:<=0188$C1E1C$,)-$!#PG$H/8$

F6:810$;:2$"189$)O$$

$

G:L1$791L8$218<C9703$;2:L$961$0:0,/9910</91=$E1287:0$:;$961$1QF12J94$6:H1E124$

H121$FC7JJ1=$70$961$WZ$MM5$10F:=703$:2$=1F:=703O$R9$78$0:9$Y0:H0$7;$961$FC7JJ703$

:FF<221=$70$961$10F:=703$/0=&:2$961$=1F:=7034$870F1$961$1;;1F98$H121$:?812E1=$70$

961$=1F:=1=$/<=7:O$"678$FC7JJ703$:FF<221=$/9$961$?79$2/918$Ui$Y?79&84$SU$Y?79&84$

/0=$-S$Y?79&8$/9$8:L1$92/0871098$:FF<22703$70$961$1QF12J9O$G70F1$961$/<=7:$C1E1C$/9$

961$70J<9$9:$961$10F:=12$F:2218J:0=8$9:$961$0:2L/C78/97:0$89/0=/2=$Z(#$I+)i4$

H67F6$70$798$9<20$F:2218J:0=8$9:$21/C$?2:/=F/89703$F:0=797:084$96181$791L8$H121$

Y1J9$/0=$<81=$70$961$C78910703$9189O$M<=7:$F:=703$6/8$?110$86:H0$9:$J2:=<F1$

:E1286::98$<J$9:$.OT$=($(Nielsen & Lundh, 2003)O$G:$;/24$<0J<?C7861=$2181/2F6$?B$:01$:;$961$/<96:28$:;$9678$J/J12$6/8$86:H0$:E1286::98$<J$9:$i$=($;:2$SU$Y?79&8O$

G11$"/?C1$T$;:2$@M(N$F:=1F$81997038$/0=$/0$:E12E71H$:;$961$?79$2/918O$

!

?'#3-![/!T26-(!$-%%*4+$!*4!?-$%!L/!

U'A-! F,#(8'44-3!#*%!&'%-!Q9#*%G$S!

",6*2!#*%!&'%-!;*4(3,6*4+!V"C=!Q9#*%G$S!

V"C!#*%!&'%-!Q9#*%G$S!

F:-(%&'3!D'46!W-:3*('%*24!;FDW=!

V'&'A-%&*(!F%-&-2!;VF=!

C"DE!\Z!;FDW<!VF=! $$Ui$ $$UTO)$ +OT$ o18$ o18$

C"DE!Y\!;FDW=! $$SU$ $$.lO-$ +OT$ o18$ A:$

C"DE!XY!;FDW=! $$-S$ $$ilO)$ +OT$ o18$ A:$

C"DE!.LZ! +)i$ ++.Oi$ )Ol$ A:$ A:$

C"DE!.YP! +S*$ +U.O+$ )Ol$ A:$ A:$

C"DE!.XL! +-)$ +lUO.$ )Ol$ A:$ A:$

$

7;,;, @A++"61$8/L1$J2:F1881=$/<=7:$96/9$H/8$;1=$709:$961$WZ$MM5$10F:=12$/0=$961$@M(N$

10F:=12$H/8$/C8:$;1=$709:$9H:$P>$8B891L8O$P>$/<=7:$K</C79B$F/0$L1/0$L/0B$

=7;;12109$967038$=1J10=703$:0$961$1K<7JL109$<81=$70$961$8730/C$F6/70$/0=$70$

J/297F<C/2$:0$961$?2:/=F/89$J2:F188:2$/0=$6:H$79$78$819$9:$J2:F188$961$/<=7:O$P:2$

9678$21/8:04$9H:$P>$8B891L8$H121$70FC<=1=$70$9678$C78910703$9189O$

$

"61$;7289$P>$8B891L4$P>$+4$F:087891=$:;$/$J2:9:9BJ1$:;$961$/<=7:$J2:F188:2$]L07/$

-S$H796$+S$?79$c5>$70J<9$/0=$:<9J<9O$]0CB$798$J8BF6:/F:<897F$L<C97JC1Q$FC7JJ12$

/0=$/$J6/81$8F2/L?C12$H121$/F97E1$/0=$0:$:9612$J2:F188703$H/8$=:01$70$961$

J2:F188:2O$"61$J6/81$8F2/L?C12$F:<C=$/C8:$?1$9<201=$:;;O$G11$81F97:0$UO)O)$;:2$

=19/7C8$:0$H610$961$J6/81$8F2/L?C12$H/8$70$<81O$":$/99/70$/0$1Q910=1=$/<=7:$

;21K<10FB$2/031$:;$/?:<9$+lO.$YWn$70$9678$J2:9:9BJ14$8703C1$87=1?/0=$L:=1$H/8$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

S$:L07//<=7:OF:L$

!"#$

%%!&'($ $ )*+),*-,+.$

$ ++$

/F97E1$;:2$961$=7;;1210F1$8730/C8$VGq!,IX$70$961$P>$L<C97JC1QO$"61$/<=7:$J2:F188:2$

/C8:$/==1=$/0$I@G$8730/C$H796$S$YWn$=1E7/97:0O$$

$

R0$961:2B4$/0$7=1/C$P>$L:=<C/97:04$=1L:=<C/97:04$/0=$21F1J97:0$H:<C=$0:9$/;;1F9$

961$J12F17E1=$/<=7:$K</C79BO$W10F14$9:$319$/8$FC:81$/8$J:887?C1$9:$/0$7=1/C$P>$

92/08L79912$/0=$21F17E12$?B$21L:E703$961$IP$J/964$961$L<C97JC1Q$:<9J<9$;2:L$961$

J2:F188:2$H/8$8/E1=$=721F9CB$9:$/<=7:$;7C18$/9$;8$q$+-)$YWn$/0=$=1F:=1=$;2:L$

L<C97JC1Q$9:$C1;9&27369$/<=7:$<8703$/$F:LL/0=$C701$<97C79BlO$":$819$961$=27E1$709:$

961$J8BF6:/F:<897F$L<C97JC1Q$FC7JJ12$8:$96/9$961$L<C97JC1Q$J:H12$/9$8:L1$J:709$

21/F61=$?<9$01E12$1QF11=1=$961$>cp$c:H12$C7L79$=1;701=$70$(GOU+)4$/$F:LL/0=$

C701$<97C79B$H/8$<81=$96/9$F/CF<C/91=$961$L/Q7L<L$:;$/$L:E703$/E12/31$:;$961$

L<C97JC1Q$J:H12O$G11$P73<21$.$;:2$/$87LJC7;71=$32/J6$:;$P>$+O$

$

$)*+,&-!]/!D32(9!6*'+&'A!25!)O!./!

$

"61$81F:0=$P>$8B891L4$P>$)4$78$:;$961$8/L1$9BJ1$/8$961$L:89$F:LL:0$P>$8B891L$

70$GH1=786$I/=7:\8$P>$019H:2YO$"678$8B891L\8$8730/C$J/96$89/291=$H796$961$8/L1$

/<=7:$J2:F188:2$/8$70$P>$+$96/9$6121$<81=$/$J8BF6:/F:<897F$C1;9&27369$FC7JJ12$/0=$

/$J6/81$8F2/L?C12$/0=$0:9$798$J8BF6:/F:<897F$L<C97JC1Q$FC7JJ12$:2$/0B$:9612$

J2:F188703O$R0$9678$/<=7:$8B891L4$961$J6/81$8F2/L?C12$F:<C=$/C8:$?1$9<201=$:;;O$

G11$81F97:0$UO)O)$;:2$=19/7C8$:0$H610$961$J6/81$8F2/L?C12$H/8$70$<81O$@1,

1LJ6/87n1=$C1;9&27369$/<=7:4$C7L791=$9:$/$+.$YWn$?/0=H7=964$/0=$/0$I@G$8730/C$

H121$;1=$709:$/$c2:;C701$@>>$89121:$10F:=12$H796$J21,1LJ6/878$10/?C1=4$/0=$/0$

/0/C:3<1$L<C97JC1Q$8730/C$;2:L$961$89121:$10F:=12$H/8$;1=$709:$/$(^$(2:/=F/89$

"p.$P>$92/08L79912O$M$T*$=($IP$/9910</9:2$9610$21=<F1=$961$:<9J<9$C1E1C$;2:L$

961$92/08L79912$709:$/0$M<=1L/9$P>,>5U$21F17E12$V<J32/=1=$70$8:;9H/21$;2:L$

P>,>5TXO$"61$L<C97JC1Q$:<9J<9$;2:L$9678$21F17E12$H/8$21F:2=1=$:0$/$

G:<0=@1E7F18$l))$<079$/9$+-)$YWn$L:0:$)U$?798O$"610$961$F:LL/0=$C701$<97C79B$

/C8:$<81=$70$P>$+$=1F:=1=$961$L<C97JC1Q$/<=7:$9:$C1;9&27369$/<=7:O$$

$

":$819$961$=27E1$709:$961$J8BF6:/F:<897F$C1;9&27369$FC7JJ12$8:$96/9$961$L<C97JC1Q$

J:H12$/9$8:L1$J:709$21/F61=$?<9$01E12$1QF11=1=$961$>cp$c:H12$C7L79$=1;701=$70$

(GOU+)4$961$8/L1$F:LL/0=$C701$<97C79B$<81=$70$P>$+$H/8$<81=$:0$L<C97JC1Q1=$

/<=7:$?1;:21$=1,L<C97JC1Q703$/0=$=1,1LJ6/878O$"61$/<=7:$8109$9:$961$89121:$

10F:=12$H/8$C1;9&27369$=1,1LJ6/87n1=$/<=7:$9:$7L79/91$961$F<22109$P>$

92/08L7887:0$F6/70$;:2$GH1=786$I/=7:O$"61$/<=7:$H/8$J21J/21=$70$/=E/0F1$/0=$

JC/B1=$?/FY$:0$/$G:<0=@1E7F18$l))$<079O$"61$I@G$8730/C$8109$9:$961$89121:$

10F:=12$H/8$/$?/0=,J/88$;7C9121=$21F:2=703$:;$/0$I@G$8730/C$JC/B1=$?/FY$:0$/$

G:<0=@1E7F18$l))$<079$/9$+-)$YWn$8/LJC1$2/91O$"61$=1E7/97:0$:;$961$P>$8730/C$

H/8$819$9:$l.$YWn4$961$I@G$C1E1C$H/8$819$9:$T$YWn4$/0=$961$8730/C$89210396$709:$961$

21F17E12$H/8$819$9:$+$L_4$/CC$<8703$L1/8<21L1098$70$961$M<=1L/9$21F17E12O$G11$

P73<21$S$;:2$/$87LJC7;71=$32/J6$:;$P>$)O$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

l$c2:E7=1=$?B$!17;$5C/188:0$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +)$

$

$)*+,&-!Y/!D32(9!6*'+&'A!25!)O!L/!

Z/F6$791L$9:$?1$<81=$70$961$C78910703$9189$;2:L$961$9H:$P>$8B891L8$H/8$;70/CCB$

/=[<891=$9:$,)-$!#PG$9:$L/9F6$961$C:<=0188$C1E1C$:;$961$@M(N$8B891L8O$Z/F6$

/<=7:$791L$H/8$/C8:$F/21;<CCB$8B0F62:07n1=$H796$:9612$791L8$:27370/9703$;2:L$961$

8/L1$1QF12J9O$

7;,;7 ABCD+-('*&+8/15%+""++R9$H/8$=78F<881=$H619612$>cZf$M<=7:$!/B12$RR$/8$<81=$70$@M($86:<C=$?1$

70FC<=1=$/L:03$961$/<=7:$8B891L8$70$9678$9189O$":$/E:7=$9::$L/0B$791L8$9:$?1$

32/=1=$?B$961$8<?[1F98$/0=$870F1$@M($<8703$>cZf$M<=7:$!/B12$RR$H7CC$J2:?/?CB$

0:9$?1$7092:=<F1=$70$GH1=10$?1B:0=$927/C84$79$H/8$=1F7=1=$0:9$9:$70FC<=1$>cZf$

M<=7:$!/B12$RRO$$$

7;,;E -#)F&%++MFF:2=703$9:$961$>#GWIM$8J1F7;7F/97:04$/9$C1/89$:01$/0F6:2$86:<C=$?1$70FC<=1=$

;:2$J:89,8F2110703$J<2J:818$/0=$9:$L/Y1$8<21$96/9$791L8$70$961$9189$F:E12$/$C/231$

J:297:0$:;$961$/<=7:$K</C79B$8F/C1O$"61$21K<721=$/0F6:2$70$961$21F:LL10=/97:0$78$

/$TO.$YWn$C:H,J/88$;7C9121=$E1287:0$:;$961$21;1210F1$8730/CO$"678$/0F6:2$6/8$?110$

<81=$70$L/0B$91898$:E12$961$B1/284$?<9$961$/0F6:2$6/8$C/91CB$?110$K<1897:01=$/8$

961$/<=7:$K</C79B$:;$L:=120$/<=7:$F:=1F8$6/8$7LJ2:E1=$87307;7F/09CBO$G70F1$L:89$

F:=1F8$91891=$9:=/B$6/E1$/$L<F6$H7=12$?/0=H7=96$/0=$1Q67?79$F:LJC191CB$

=7;;12109$/291;/F98$F:LJ/21=$9:$/$TO.$YWn$C:H,J/88$;7C9121=$E1287:04$9678$/0F6:2$

/CH/B8$89/0=8$:<9$1E10$7;$/$9189$791L$6/8$81E121$F:=703$/291;/F98O$"678$78$961$

21/8:0703$37E10$?B$961$@&@M(M$32:<J$70$961$Z(#$21J:29$(cA$*-U$9:$1QJC/70$H6B$

/$01H$Y70=$:;$/0F6:2$96/9$=:18$0:9$1LJC:B$?/0=$C7L79/97:0$H/8$=1E1C:J1=$/0=$

<81=O$$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +T$

$

P:2$961$8/L1$21/8:084$961$/0F6:2$=1E1C:J1=$?B$961$@&@M(M$32:<J$70$961$

>#GWIM$9189$=18F27?1=$70$9678$J/J12$H/8$<81=O$"678$01H$/0F6:2$78$F21/91=$?B$/$

87LJC1$/C3:2796L$96/9$F:L?7018$9H:$9BJ18$:;$8730/C$7LJ/72L109$9:$21;C1F9$F<22109$

9BJ7F/C$F:=703$/291;/F98O$"61$;7289$7LJ/72L109$78$>@5",?/81=$K</0978/97:0$

=789:297:0$/0=$961$81F:0=$78$89121:$7L/31$=789:297:0O$"678$/C3:2796L$78$=18F27?1=$

70$=19/7C$70$(cA$*-U$/0=$70$/0$:J10$8:<2F1$7LJC1L109/97:0iO$"61$J/2/L1912$

81997038$<81=$70$9678$9189$H121$961$8/L1$/8$<81=$;:2$961$@&@M(M$91898$V=789:297:0$

819$9:$+*$/0=$;<nnB$9:$*O).X (EBU, 2009)O$"61$/0F6:2$791L8$H121$/=[<891=$9:$,)-$!#PG$9:$L/9F6$961$C:<=0188$;2:L$961$:9612$791L8$/0=$961$/0F6:2$791L8$H121$/C8:$

F/21;<CCB$8B0F62:07n1=$H796$:9612$791L8$:27370/9703$;2:L$961$8/L1$1QF12J9O$

E 8*3$5#*#4+$53$3+9+65$F&'+"61$C78910703$91898$H121$J12;:2L1=$9:$70E18973/91$961$J12F17E1=$/<=7:$K</C79B$:;$

791L8$:27370/9703$;2:L$1QF12J98$J2:F1881=$962:<36$961$=7;;12109$F:=703$

J2:F18818$=18F27?1=$70$G1F97:0$TO$"61$/88188L109$L196:=$70$"189$+$H/8$961$R"#,

I$(GO+++S$21F:LL10=/97:0$(ITU, 1997)`$961$/88188L109$L196:=$70$"189$)$H/8$961$R"#,I$(GO+.TU$V>#GWIMX$(ITU, 2003)O$R0$9678$81F97:04$;<29612$=19/7C8$:0$961$C78910703$91898$H7CC$?1$J2:E7=1=O$

$

(GO+++S$H/8$F6:810$9:$=191F9$1QJ1F91=$8L/CC$=7;;1210F18$?19H110$961$8B891L8$

91891=$70$"189$+4$H6121/8$>#GWIM$H/8$<81=$;:2$961$1QJ1F91=$?73312$K</C79B$

=7;;1210F18$=<1$9:$961$70FC<87:0$:;$961$C:H$?79$2/91$8B891L8$70$"189$)O$"189$+$H/8$

/CH/B8$J12;:2L1=$;7289$?B$961$8<?[1F98$=<1$9:$961$1QJ1F91=$8L/CC12$=7;;1210F18$

?19H110$F:=1F8$96<8$/E:7=703$/$6/2=12$9/8Y$/9$961$10=$H610$C7891012$;/973<1$

F:<C=$70;C<10F1$961$218<C98O$

E;! G(2H5)$3++MCC$961$8<?[1F98$VAqT*X$6/=$1QJ12710F1$C78910703$F2797F/CCB$9:$21J2:=<F1=$8:<0=O$

"61B$H121$21F2<791=$/9$9H:$C:F/97:08$70$GH1=10O$R0$G9:FY6:CL4$961$8<?[1F98$

V0q+l`$/31=$)+$9:$S)`$L1/0qU+$B1/28`$L1=7/0qU+X$70FC<=1=$J128:001C$;2:L$

GH1=786$I/=7:$/8$H1CC$/8$70=1J10=109$C78910128$/C96:<36$L:89$C78910128$H121$

J2:;1887:0/C$8:<0=$103701128O$M9$961$81F:0=$C:F/97:04$961$GF6::C$:;$><87F$/9$

c791D4$/$F/LJ<8$/9$!<C1D$#07E12879B$:;$"1F60:C:3B4$961$8<?[1F98$V0q+T`$/31=$+-$9:$

)-`$L1/0q)T`$L1=7/0q)+X$/CC$6/=$/<=7:$91F60:C:3B$1=<F/97:0$/0=$H121$179612$

89<=1098$:2$/C<L07$/9$961$8F6::CO$G7Q$:;$961$8<?[1F98$6/=$<0=123:01$61/2703$91898$

70$96172$/=L7887:0$91898$;:2$96172$1=<F/97:0/C$J2:32/LO$"61$21L/70703$8<?[1F98$

H121$/88<L1=$9:$6/E1$213<C/2$61/2703$V0:01$:;$961L$89/91=$/$Y0:H0$61/2703$

C:88$?1;:21$961$9189XO$$

E;, B%&4%/665+6/$5%*/0+"61$31012/C$F279127/$;:2$81C1F97:0$:;$1QF12J98$F/0$?1$8<LL/2781=$/8$;:CC:H8O$

$

• "61$1QF12J98$86:<C=$8J/0$/$?2:/=$2/031$:;$=7;;12109$9BJ18$:;$L/9127/C$/0=$

L<87F/C$310218O$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

i$8:<2F1;:231O019&J2:[1F98&/0F6:2$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +U$

• "61$1QF12J98$86:<C=$F:L1$;2:L$9BJ7F/C$J2:32/LL1$L/9127/CO$

• "61$1QF12J98$86:<C=$FC1/2CB$21E1/C$8:L196703$/?:<9$961$J12;:2L/0F1$:;$

:01$:2$L:21$:;$961$8B891L8$/0=$:;$=7;;1210F18$?19H110$961$8B891L8O$

• "6121$L<89$0:9$?1$/0B$?7/8$9:H/2=8$:2$/H/B$;2:L$/0B$J/297F<C/2$8B891LO$

• "61$1QF12J98$86:<C=$?:96$?1$L/9127/C$J21E7:<8CB$<81=$70$:9612$C78910703$

91898$V9:$F:LJ/21$H796$:9612$91898X$/0=$L/9127/C$0:9$J21E7:<8CB$<81=$70$

:9612$C78910703$91898$V9:$/E:7=$961$J:887?7C79B$96/9$F:=1F8$L7369$?1$9<01=$

9:$961$1QF12J98$81C1F91=$;:2$961$9189XO$

• "61$1QF12J98$86:<C=$0:9$?1$H1/278:L1$:2$9::$70E:CE703O$

E;,;! <53$+!+G70F1$961$:?[1F97E18$:;$9678$9189$H121$9:$70E18973/91$961$/<=7:$K</C79B$21/F61=$/9$

961$673612$?79$2/918$70$@M(N$/0=$9:$F:LJ/21$218<C98$;2:L$9678$C78910703$9189$H796$

218<C98$;2:L$Z(#$(cA$*-U$(EBU, 2009)4$;:<2$:;$961$L:89$F2797F/C$1QF12J98$<81=$70$(cA$*-U$H121$81C1F91=$;:2$9678$9189O$R0FC<=703$/CC$961$791L8$;2:L$961$J21E7:<8$9189$

VH67F6$H/8$J12;:2L1=$/FF:2=703$9:$961$>#GWIM$21F:LL10=/97:0X$H:<C=$6/E1$

L/=1$961$C10396$:;$961$9189$7LJ2/F97F/CO$G70F1$961$8/L1$10F:=12$<81=$;:2$(cA$*-U$

H/8$<81=$;:2$9678$91894$96121$H/8$0:$J/297F<C/2$011=$9:$811Y$01H$1QF12J98O$"61$

;:<2$1QF12J98$81C1F91=$/21$;:<0=$70$"/?C1$UO$"61$1QF12J9$aGJ11F6$VJ/0X$N$.Q$!)b$78$

961$1QF12J9$aGJ11F6$VJ/0Xb$/;912$;7E1$10F:=7038$/0=$=1F:=7038$:;$>cZf,+$M<=7:$

!/B12$RR$/8$=18F27?1=$70$G1F97:0$TO+O$M8$J21E7:<8CB$L1097:01=4$J2:=<F97:0$:;910$

70FC<=18$/$0<L?12$:;$F/8F/=18O$W10F14$:01$8<F6$1QF12J9$H/8$70FC<=1=O$

$

?'#3-!\/!0@(-&:%$!,$-6!*4!?-$%!./!

U'A-!25!-@(-&:%! U'A-!*4!DVU!PX\! C-$(&*:%*24! ^-4+%8! _::-&!5&-`,-4(B!3*A*%!

-::0/(35+I+/##&(#)5%+

"#$%%&$'()*!$+,!-).$&)!$++/'+0)1!

MJJC/<81$H796$;1L/C1$

/00:<0F12O$

+SO.$8$ +iOT$YWn$

J0/33*)/0+ -#21$((*!34.%$+4!$+,!0$(3$+)3(!

(2/884$97LJ/07$/0=$F/89/01984$

;2:L$>/0<1C$=1$P/CC/\8$?/CC19$0()1,2'#"#,)3")%#"4)!&$,4O$"/Y10$;2:L$92/FY$+$:0$961$GM5@$W>5$

i*+S*S$;2:L$W/2L:07/$><0=7O$

+lOi$8$ )U$YWn$

K&(35! $#)&)031/!%/%$ ZQF12J9$;2:L$961$6:<81&J:J$

8:03$a!:E1$78$f:01b$?B$@/E7=$

f<199/O$

)*O+$8$ ))$YWn$

G:55)F+L:/#M! 2#-).$&)!(%))05!67),4(5$

P1L/C1$8J11F6$;2:L$/$GH1=786$

01H8F/89$J/001=$

8C7369CB$9:$961$27369O$

+SO*$8$ )U$YWn$

G:55)F+L:/#M+?+NO+8,! A:9$70$(cA$*-U$ GJ11F6$VJ/0X$/;912$;7E1$

10F:=7038$/0=$=1F:=7038$:;$

>cZf,+$M<=7:$!/B12$RR$/9$TiU$

Y?79&8O$

+SO*$8$ )*O.$YWn$

$

E;,;, <53$+,+G70F1$961$:?[1F97E18$:;$"189$)$H121$9:$/88188$21/C7897F$P>$/0=$@M(N$8B891L8$

<8703$=7;;12109$=1;70797:08$:;$/<=7:$K</C79B$/0=$9:$F:LJ/21$P>$/0=$@M(N$

8B891L84$/<=7:$F:09109$H/8$;7289$F:CC1F91=$96/9$H/8$96:<369$9:$?1$F2797F/C$;:2$:01$

:2$L:21$:;$961$8B891L8$70$961$9189O$"61$81E10$6:<28$:;$/<=7:$;2:L$961$=7;;12109$

F/913:2718$/0=$L<87F/C$310218$96/9$H121$8:291=$709:$961$9H:$/<=7:$J2:F188703$

F/913:2718$=10:91=$ac)b$/0=$acTb$V/8$=18F27?1=$70$G1F97:0$TO)X$H121$21=<F1=$

<8703$961$F279127/$70$G1F97:0$UO)O$"61$/<=7:$J2:F188703$;:2$1/F6$F/913:2B$H/8$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +.$

J12;:2L1=$/0=$961$J2:F1881=$/<=7:$H/8$;1=$962:<36$961$8B891L8$=18F27?1=$70$

G1F97:0$TO)O$"61$:<9J<98$;2:L$961$=7;;12109$8B891L8$H121$9610$C:<=0188$/C7301=$

/0=$8B0F62:07n1=$H796$961$:27370/C$J2:F1881=$/<=7:OO$

$

#8703$961$92/70703$L:=1$70$961$8:;9H/21$G"Zc$V811$G1F9O$UOUX4$79$H/8$J:887?C1$9:$

1/87CB$8H79F6$?19H110$961$:27370/C$J2:F1881=$/<=7:$/0=$961$:<9J<98$;2:L$961$

=7;;12109$8B891L84$L/709/70703$97L1$8B0F62:07n/97:0O$R9$H/8$9610$0:91=$H67F6$

/<=7:$F:09109$H/8$961$L:89$F2797F/C$;:2$961$=7;;12109$8B891L8O$M0$:E12E71H$:;$961$

910$81C1F91=$1QF12J98$F/0$?1$;:<0=$70$"/?C1$.O$$

$

?'#3-!]/!0@(-&:%$!,$-6!*4!?-$%!L/!

U'A-!25!-@(-&:%!

V&-I$-%!

C-$(&*:%*24! ^-4+%8! _::-&!5&-`,-4(B!3*A*%!

",6*2!:&2I(-$$*4+!('%-+2&B!

V8'$-!$(&'A#3-&!*4!)O!$B$%-A$!

T2AA24'3*%*-$!7*%8!-@(-&:%$!*4!?'#3-!\<!7*%8!%8-!-@(-:%*24!25!%8-!',6*2!:&2(-$$*4+!

"::3',$-!7!'442,4(-&!

c)$ MJJC/<81$H796$;1L/C1$

/00:<0F12O$

+SOU$8$ +iOU$YWn$ c)$ ]0$ G/L1$/8$aMJJC/<81$H$

/00:<0F12a$

T3'$$*('3! c)$ (2/884$97LJ/07$/0=$F/89/01984$

;2:L$>/0<1C$=1$P/CC/\8$?/CC19$

0()1,2'#"#,)3")%#"4)!&$,4O$"/Y10$;2:L$92/FY$+$:0$961$

GM5@$W>5$i*+S*S$;2:L$

W/2L:07/$><0=7O$

+lOi$8$ )U$YWn$ c)$ ]0$ G/L1$/8$a5C/887F/Cb$

03-(%&24*(! c)$ ZQF12J9$H796$892:03$

92/0871098$;2:L$a_/769:E729/b$

:0$961$/C?<L$5-(%,!&&#&$?B$c/0$G:07FO$

).O.$8$ ))$YWn$ c)$ ]0$ $

03-(%&24*(!;'%%=! c)$ G/L1$/8$ZC1F92:07F$?<9$

/9910</91=$?B$))$=(O$"6/9$784$

961$C:<=0188$78$,.+$!#PGO$

).O.$8$ ))$YWn$ c)$ ];;$ $

a2,$-! cT$ ZQF12J9$;2:L$961$6:<81&J:J$

8:03$a!:E1$78$f:01b$?B$@/E7=$

f<199/O$

+-Oi$8$ ))$YWn$ cT$ ]0$ G/L1$/8$aW:<81a$

V2:b-4%! $ "61$7092:$9:$961$8:03$a@:L$

M0=2/b$?B$%109O$

+-OS$8$ )+$YWn$ cT$ ]0$ $

V2:W2@! cT$ M$F6:2<8$70$961$8:03$aP/=703$

!7Y1$M$PC:H12b$?B$I:Q1991O$$

)UO.$8$ ))$YWn$ cT$ ]0$ $

F:--(8^!;42!:'4=!

cT$ G/L1$/8$GJ11F6!$VJ/0X$/?:E1$

?<9$H796:<9$961$J/00703O$

)SOU$8$ )U$YWn$ cT$ ]0$ $

F:--(8^!;:'4=! cT$ P1L/C1$8J11F6$VC:03X$;2:L$/$

GH1=786$01H8F/89$J/001=$

8C7369CB$9:$961$27369O$

)SOU$8$ )U$YWn$ cT$ ]0$ M$C:0312$E1287:0$:;$

aGJ11F6$VJ/0Xb$

K2&36! c)$ ZQF12J9$H796$6/0=FC/J8$/0=$

0:9$L<F6$:9612$J12F<887:0$

;2:L$961$8:03$a>732/97:0b$:0$

961$/C?<L$6+%#,3*$&+/)?B$A7970$G/H601BO$

)+OU$8$ ))$YWn$ c)$ ]0$ $

$

c21819$ac)b$70=7F/918$/<=7:$J2:F188703$F/913:2B$c)`$7O1O4$96/9$961$/<=7:$J2:F188703$

H/8$=:01$<8703$:0CB$/0$/=E/0F1=$:01$?/0=$/<=7:$J2:F188:2$96/9$=7=$/$L707L<L$

:;$86:29$912L$C7L79703$/0=$L/70CB$C:H$C1E1C$F:LJ21887:0O$c21819$acTb$70=7F/918$

/<=7:$J2:F188703$F/913:2B$cT`$7O1O4$961$/<=7:$J2:F188703$H/8$=:01$<8703$961$8/L1$

:01$?/0=$/<=7:$J2:F188:2$/8$;:2$961$c)$F/913:2B4$/3/70$=:703$/$L707L<L$:;$86:29$

912L$C7L79703$?<9$L:21$C:H$C1E1C$F:LJ21887:04$;:CC:H1=$?B$/$8:;9H/21$L<C97?/0=$

/<=7:$J2:F188:2$<8703$/$J21819$=187301=$9:$C7L79$798$70;C<10F1$:0$961$/<=7:$8730/CO$

"61$/<=7:$J2:F188703$;:2$?:96$F/913:2718$/21$=18F27?1=$70$L:21$=19/7C$70$81F97:0$

TO)O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +S$

GJ11F6!4$/87=1$;2:L$961$/<=7:$J2:F188703$cT4$78$961$8/L1$/<=7:$/8$GJ11F6$VJ/0X$

70$"189$+$JC<8$/0$/==797:0/C$+*OU$8$:;$/<=7:$;2:L$961$8/L1$01H8F/89O$

$

ZC1F92:07F$V/99X$78$961$8/L1$/<=7:$/8$ZC1F92:07F$?<9$/9910</91=$?B$))$=($9:$L/Y1$

8<21$96/9$0:$/<=7:$21/F61=$961$FC7J$C1E1C$:;$961$J21,1LJ6/878$FC7JJ128$70$961$P>$

8B891L8O$"678$1QF12J9$F:09/708$6736,C1E1C$92/0871098$/9$6736$;21K<10F7184$/$

F:0=797:0$96/9$1QJC/708$H6B$961$/9910</97:0$H/8$F6:810$9:$?1$8:$6736O$M;912$

;11=703$9678$/9910</91=$1QF12J9$96:<36$961$=7;;12109$8B891L84$961$C1E1C$H/8$

2189:21=$962:<36$/LJC7;7F/97:0$:;$961$218<C9703$791L8$9:$L/9F6$961$C:<=0188$:;$

961$:9612$791L8O$

$

"61$J6/81$8F2/L?C12$70$961$P>$/<=7:$J2:F188:2$L/Y18$/8BLL1927F$/<=7:$

H/E1;:2L8$L:21$8BLL1927F$/0=$96<8$C188$8108797E1$9:$J21,1LJ6/878$FC7JJ703O$

"61$21/8:0$;:2$798$<81$78$96/9$F129/70$9BJ18$:;$8730/C8$8<F6$/8$8J11F6$F/0$6/E1$

673612$J1/Y$C1E1C8$:0$961$J:8797E1$87=1$:;$961$H/E1;:2L$96/0$:0$961$013/97E1$87=1$

:2$E7F1$E128/4$/0=$9678$/8BLL192B$F/0$?1$21=<F1=$?B$961$J6/81$8F2/L?C12O$^610$

L:21$8BLL192B$78$21/F61=4$961$6736189$J1/Y$C1E1C8$/21$<8</CCB$21=<F1=O$

ZQJ12710F1$86:H8$96/9$961$<81$:;$961$J6/81$8F2/L?C12$L/B$F/<81$K</C79B$

/C912/97:08O$W10F14$961$J6/81$8F2/L?C12$H/8$10/?C1=$;:2$/CC$1QF12J98$1QF1J9$

ZC1F92:07F$V/99X$/8$9678$H/8$/9910</91=O$"61$1QF12J9$ZC1F92:07F$V/99X$H/8$=187301=$

0:9$9:$9273312$961$J21,1LJ6/878$FC7JJ12$/0=$96<8$961$J6/81$8F2/L?C12$H/8$0:9$

01F188/2B$/0=$96121;:21$9<201=$:;;O$

$

(1C:H$78$/$C789$:;$961$81C1F97:0$:;$1QF12J98$;:2$"189$)d$

• P7E1$1QF12J98$70$961$c)$/<=7:$J2:F188703$F/913:2B$/0=$;7E1$1QF12J98$70$961$

cT$F/913:2B`$

• "6211$1QF12J98$F:09/70703$L:=120$L/708921/L$L<87F$/0=$;:<2$1QF12J98$

F:09/70703$0:0,L/708921/L$L<87F`$

• ]01$1QF12J9$F:09/70703$/JJC/<81`$

• ]01$1QF12J9$F:09/70703$J/001=$8J11F6`$

• ]01$1QF12J9$F:09/70703$0:0,J/001=$8J11F6`$

• "H:$E1287:08$:;$/0$1QF12J9$H796$892:03$92/0871098$H796$=7;;12109$C1E1C8$9:$

70E18973/91$6:H$961$J21,1LJ6/878$FC7JJ12$70;C<10F18$961$32/=703$:;$961$P>$

8B891L8`$

• P:<2$1QF12J98$96/9$/214$/87=1$;2:L$961$/<=7:$J2:F1887034$961$8/L1$/8$70$

"189$+$/0=$70$(cA$*-U`$/0=$

• G:L1$1QF12J98$F:09/70703$/$L:=12/91$/L:<09$6736$;21K<10FB$10123B$/0=$

1QF12J98$F:09/70703$L<F6$6736$;21K<10FB$10123BO$

MCC$1QF12J98$H121$1=791=$9:$6/E1$8L::96$?137007038$/0=$10=8$/0=$H121$

F/C7?2/91=$9:$,)-$!#PG$8:$/8$9:$6/E1$961$8/L1$C:<=0188O$G11$G1F97:0$T$;:2$L:21$

70;:2L/97:0O$

$

E;7 G13$563+(#'5%+$53$+9+3(66/%1++"61$8B891L8$70FC<=1=$70$961$9H:$91898$H121$=18F27?1=$70$=19/7C$70$G1F97:0$TO$"61$

8B891L8$/21$8<LL/27n1=$70$961$;:CC:H703$8<?81F97:08O$

E;7;! <53$+!+"61$F:=1F8$/0=$96172$J2:J129718$70$"189$+$/21$;:<0=$70$"/?C1$SO$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +l$

$

?'#3-!Y/!T26-($!*4!?-$%!./!

U'A-! FB$%-A! ",6*2!#G7!Q9aNS.!

F,#(8'44-3!#*%!&'%-!Q9#*%G$S!

",6*2!#*%!&'%-!;*4(3/!V"C=!Q9#*%G$S!

V"C!#*%!&'%-!Q9#*%G$S!

F:-(%&'3!D'46!W-:3*('%*24!;FDW=!

V'&'A-%&*(!F%-&-2!;VF=!

C"DE!XY!;FDW=! @M(N$ ))OU$ $$-S$ $$ilO)$ +OTT$ o18$ A:$

C"DE!.LZ! @M(N$ +.OT,

+SO)$

+)i$ ++.Oi$ )O.T$ A:$ A:$

C"DE!.YP! @M(N$ +.OT,

+SO)$

+S*$ +U.O+$ )O.T$ A:$ A:$

C"DE!.XL! @M(N$ +lO*,

+-O.$

+-)$ +lUO.$ )O.T$ A:$ A:$

+$>1/8<21=$?/0=H7=96O$R;$9H:$E/C<18$:FF<24$9610$L:89$/<=7:$21/F618$961$C:H12$E/C<1$H67C1$92/0871098$F/0$

21/F6$961$673612$E/C<1O$

E;7;, <53$+,+"61$8B891L8$/0=$96172$J2:J129718$70$"189$)$/21$;:<0=$70$"/?C1$lO$

$

?'#3-!c/!FB$%-A$!*4!?-$%!L/!

U'A-! FB$%-A! ",6*2!#G7!Q9aNS.!

F,#(8'44-3!#*%!&'%-!Q9#*%G$S!

",6*2!#*%!&'%-!;*4(3/!V"C=!Q9#*%G$S!

V"C!#*%!&'%-!Q9#*%G$S!

F:-(%&'3!D'46!W-:3*('%*24!;FDW=!

V'&'A-%&*(!F%-&-2!;VF=!

C"DE!\Z!;FDW<!VF=! @M(N$ +iOl$ $$Ui$ $$UTO)$ +OT$ o18$ o18$

C"DE!Y\!;FDW=! @M(N$ +iOl$ $$SU$ $$.lO-$ +OT$ o18$ A:$

C"DE!XY!;FDW=! @M(N$ ))OU$ $$-S$ $$ilO)$ +OT$ o18$ A:$

C"DE!.LZ! @M(N$ +.OT,

+SO)$

+)i$ ++.Oi$ )Ol$ A:$ A:$

C"DE!.YP! @M(N$ +.OT,

+SO)$

+S*$ +U.O+$ )Ol$ A:$ A:$

C"DE!.XL! @M(N$ +lO*,

+-O.$

+-)$ +lUO.$ )Ol$ A:$ A:$

)O!.! P>$89121:$ +lO.$ $ $ $ $ $

)O!L! P>$89121:$ +.O*$ $ $ $ $ $

"4(82&! $ )UO*$ $ $ $ $ $

a*66-4!&-5-&-4(-! $ )UO*$ $ $ $ $ $

+$>1/8<21=$?/0=H7=96O$R;$9H:$E/C<18$:FF<24$9610$L:89$/<=7:$21/F618$961$C:H12$E/C<1$H67C1$92/0871098$F/0$

21/F6$961$673612$E/C<1O$

E;E C.(*:65#$+/#'+0*3$5#*#4+5#P*%&#65#$++"61$C78910703$1K<7JL109$H/8$81C1F91=$9:$L/9F6$961$J21E7:<8$(cA$*-U$9189$(EBU, 2009)$(ITU, 1990)O$W1/=J6:018$H121$<81=$9:$21=<F1$961$J:887?C1$=7;;1210F18$?19H110$2::L$F6/2/F9127897F8$:;$961$9H:$9189$87918O$"61$61/=J6:01$/LJC7;712$

f2/F1$@18730$L-*T$F:09/701=$/$@$9:$M$F:0E12912$<8703$/0$/FF<2/91$709120/C$FC:FYO$

@7;;<81,;71C=$Zr$H/8$1LJC:B1=$9:$/F671E1$961$F:221F9$;21K<10FB$218J:081$70$961$

61/=J6:018$VG100617812$W@,S.*XO$"61$;7C912$F6/2/F9127897F8$H121$=1E1C:J1=$?B$

961$RI"$;:2$(cA$*-U$/0=$H121$/=/J91=$;:2$961$W@,S.*$61/=J6:018$8J1F7;7F/CCBO$

"61$;7C912$F6/2/F9127897F8$H121$1LJC:B1=$70$96181$91898$?B$J2:F188703$961$/<=7:$

;7C18$<8703$F:0E:C<97:0$/9$/$6736$218:C<97:0 (ITU, 1990)O$"678$/LJC7;712$H/8$F:001F91=$9:$961$G"Zc$8:;9H/21-4$E1287:0$+O*i/4$E7/$/$#G($70912;/F1O$'79912$

J12;:2L/0F1$H/8$L1/8<21=$70$961$/0/C:3<1$=:L/70$H796$961$M<=7:$c21F787:0$

GB891L$"H:$[79912$8730/CO$"61$E/C<18$H121$;:<0=$9:$?1$70$961$8/L1$:2=12$:;$

L/3079<=1$:2$?19912$96/0$:9612$6736,J12;:2L/0F1$F:0E129128O$

$$

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$

-$HHHO/<=7:2181/2F6C/?8OF:L$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +i$

"61$0:781$C1E1C8$:;$961$C78910703$2::L8$H121$H1CC$<0=12$AI$+.$/9$c791D$/0=$<0=12$

AI$)*$70$G9:FY6:CLO$

$

E;N <%/*#*#4++G<?[1F98$70E791=$9:$961$C78910703$9189$H121$8109$/$C70Y$9:$/<=7:$;7C18$96/9$961B$H121$

/8Y1=$9:$C78910$9:$/9$6:L1$;:2$92/70703$J<2J:818$?1;:21$961$91898O$"61$/<=7:$;7C18$

F:09/701=$/CC$/<=7:$96/9$961$8<?[1F98$H121$9:$32/=1$70$961$/F9</C$91898O$ZE12B96703$

96/9$H/8$0:9$961$:27370/C$/<=7:$H/8$21C/?1CC1=$/8$2/0=:L$0<L?128$8:$/8$0:9$9:$

37E1$/0B$FC<18$:;$961$8B891L8$961B$6/=$J/881=$962:<36O$ZE12B$8<?[1F9$21F17E1=$

/0=$H/8$/8Y1=$9:$<81$9H:$2/0=:L7n1=$JC/BC7898$;:2$961$/<=7:$;7C18O$R;$8:L1$

8<?[1F98$<81=$JC/B?/FY$1K<7JL109$:;$E/2B703$K</C79B$:2$F6::81$0:9$9:$C78910$9:$/CC$

/<=7:$;7C184$9678$J2:F1=<21$H:<C=$=78927?<91$961$92/70703$81887:08$2/0=:LCB$:E12$

/CC$/<=7:$;7C18O$

$

"61$/F9</C$91898$H121$J21F1=1=$?B$92/70703$96/9$F:087891=$:;$32/=703$/$2/0=:L$

8<?819$:;$961$/<=7:$96/9$H/8$9:$?1$32/=1=$C/912$70$961$/F9</C$9189O$"678$H/B$1/F6$

8<?[1F9$C1/201=$961$;<0F97:08$:;$961$<812$70912;/F14$?1F/L1$;/L7C7/2$H796$961$

C78910703$10E72:0L109$/0=$1K<7JL1094$/0=$J2/F97F1=$32/=703$8:L1$:;$961$/<=7:$

;2:L$961$/F9</C$9189O$"61$92/70703$;:2$"189$+$V(GO+++SX$F:087891=$:;$/$8<?819$

F:LJ278703$;7E1$32/=70384$2/0=:LCB$81C1F91=$;:2$1/F6$8<?[1F9O$Z/F6$:;$961$;7E1$

1QF12J98$/JJ1/21=$:0F14$96211$:;$961$;:<2$?79$2/918$/JJ1/21=$:0F14$/0=$:01$?79$

2/91$/JJ1/21=$9H7F1O$"61$92/70703$;:2$"189$)$V>#GWIMX$F:087891=$:;$/$2/0=:LCB$

81C1F91=$8<?819$:;$96211$1QF12J98$J2:F1881=$70$/CC$E1287:08O$Z/F6$8<?819$H/8$

2/0=:LCB$81C1F91=$9:$=78927?<91$961$92/70703$2/0=:LCB$:E12$/CC$8<?[1F98O$

$

E;Q B%&)5'(%5++"H:$9189$C1/=128$H:2Y1=$9:319612$=<2703$961$9189O$M9$961$97L1$:;$961$918984$1/F6$

8<?[1F9$21F17E1=$961$8/L1$70892<F97:08$/0=$1E12B:01$H/8$70892<F91=$?B$961$8/L1$

9H:$9189$C1/=128O$R0$/CL:89$/CC$F/8184$?:96$9189$C1/=128$H121$J218109O$R0$9678$H/B4$

961$9H:$9189$C1/=128$F:LJC1L1091=$1/F6$:9612$9:$108<21$96/9$/CC$8<?[1F98$

21F17E1=$E12B$87L7C/2$:2/C$70892<F97:08$?1;:21$961$9189O$"61$9189$C1/=128$<81=$/$

F61FYC789$9:$L/Y1$961$70892<F97:08$/8$F:08789109$/8$J:887?C1O$"61$9189$H/8$9:9/CCB$

?C70=$9:$961$C78910128O$"61$9189$C1/=128$H121$F/21;<C$0:9$9:$70;C<10F1$961$8<?[1F98$

70$6:H$961B$32/=1=$=7;;12109$/291;/F98$/0=$961$9BJ1$:;$8B891L8$:2$F:=1F8$91891=$

H121$01E12$L1097:01=O$

$

Z/F6$8<?[1F9$;7289$92/701=$;:2$"189$+$V(GO+++SX$?1;:21$J12;:2L703$"189$+$/0=$

92/701=$;:2$"189$)$V>#GWIMX$?1;:21$J12;:2L703$"189$)O$A:2L/CCB4$/$?21/Y$H796$

F:;;11$:2$91/$/0=$8/0=H7F618$H/8$J2:E7=1=$/;912$961$92/70703$;:2$"189$)O$

$

"61$8<?[1F98$H121$/CC:H1=$9:$/=[<89$961$C78910703$C1E1C8$/9$/0B$97L1$=<2703$961$

91898O$W:H1E124$961$C:<=0188$/C730L109$J12;:2L1=$?1;:21$961$1QJ127L109$

811L1=$9:$21L:E1$J:887?C1$C:<=0188$=7;;1210F18$/8$:0CB$E12B$;1H$/0=$8L/CC$

/=[<89L1098$H121$L/=1$?B$961$C78910128O$

E;Q;! <53$+!+"61$;:CC:H703$70;:2L/97:0$/0=$70892<F97:08$H121$37E10$9:$961$C78910128$?1;:21$961$

(GO+++S$9189d$

!"#$

%%!&'($ $ )*+),*-,+.$

$ +-$

$

+O M$31012/C$?/FY32:<0=$9:$961$9189O$)O M0$:E12E71H$:;$961$9189$L196:=:C:3B$/0=$<812$70912;/F1O$TO ":$9/Y1$213<C/2$?21/Y8O$UO #8/31$:;$961$C::J703$;<0F97:0/C79B$H/8$:0CB$/CC:H1=$H610$/$8<?[1F9$6/=$

C789101=$9:$/CC$/<=7:$;2:L$?13700703$9:$10=O$

.O G<?[1F98$H121$70;:2L1=$/?:<9$961$C1E1C$1K</C78/97:0$/JJC71=O$"61B$H121$10F:<2/31=$9:$819$/$F:L;:29/?C1$C78910703$C1E1C$/0=$H121$/C8:$/CC:H1=$9:$

/=[<89$961$C78910703$C1E1C$/9$/0B$97L1$=<2703$961$9189O$

SO "61$/<=7:$K</C79B$F6/031$/8$/$H6:C1$e$7O1O4$a?/87F$/<=7:$K</C79Bb$e$86:<C=$?1$F:087=121=$H610$=1F7=703$:0$/$32/=14$H67F6$L1/08$96/9$/CC$/<=7:$

K</C79B$=7;;1210F18$;2:L$961$21;1210F1$86:<C=$?1$F:087=121=$9:$?1$/<=7:$

K</C79B$=132/=/97:08O$

lO ]0CB$961$=132/=/97:08$70$/<=7:$K</C79B$86:<C=$?1$F:087=121=$H610$=1F7=703$:0$/$32/=1$/0=$0:9$6:H$3::=$961$L7Q$78$:2$6:H$961$C7891012$

10[:B8$961$F:09109O$

iO "61$C7891012$86:<C=$?1$F/21;<C$0:9$9:$J<CC$961$H2:03$8C7=12$H610$37E703$/$32/=1O$

-O R9$78$0:9$J:887?C1$9:$3:$?/FY$/0=$F6/031$/$J21E7:<8CB$37E10$32/=1O$+*O !78910128$86:<C=$L/Y1$/$0:91$:;$961$C78910703$C1E1C8$961B$<81=O$

!

E;Q;, <53$+,+"61$;:CC:H703$70;:2L/97:0$/0=$70892<F97:08$H121$37E10$9:$961$C78910128$?1;:21$961$

>#GWIM$9189d$

$

+O "61$8/L1$70892<F97:08$/8$;:2$"189$+4$?<9$0:H$961$9189$L196:=:C:3B$/0=$<812$70912;/F1$=18F27?1=$961$>#GWIM$9189O$

)O "61$C7891012$F/0$F:LJ/21$/CC$J2:F1881=$E1287:08$:;$961$/<=7:$H796$961$21;1210F1$/0=$/C8:$F:LJ/278:08$F/0$?1$L/=1$?19H110$961$=7;;12109$

J2:F1881=$E1287:08$:;$961$/<=7:O$

TO "61$/<=7:$70$"189$)$78$0:2L/C7n1=$9:$/$C:H12$C1E1C$96/0$;:2$"189$+O$$

E;Q;7 "#$5%P*5I3++R0912E71H8$H121$F:0=<F91=$V:01$70912E71H$J12$8<?[1F9X$/;912$961$C78910703$91898O$

P:2$961$J<2J:81$:;$9678$J/J124$;:<2$:;$961$70912E71H84$9H:$;2:L$1/F6$9189$87914$

H121$2/0=:LCB$81C1F91=$;:2$/0/CB878O$"61$L196:=:C:3B$<81=$6/=$/$

J610:L10:C:37F/C$/JJ2:/F6$;:2$7=1097;B703$961$8<?[1F9\8$1QJ12710F1$(Merriam, 2009)O$R0$9678$89<=B4$9678$21;128$9:$961$<0=12CB703$892<F9<21$:;$H6/9$=132/=/97:08$961$8<?[1F98$J12F17E1=$/0=)6:H)961$=132/=/97:08$H121$J12F17E1=O$M0$7LJC7F/97:0$:;$/JJCB703$/$K</C79/97E1$L196:=$78$96/9$961$218<C98$F/00:9$01F188/27CB$?1$/JJC71=$

9:$961$31012/C$J<?C7F4$?<9$79$37E18$/0$7087369$709:$<0=1289/0=703$961$8<?[1F98\$

1QJ12710F1$/0=$961$91891=$/<=7:\8$J12F17E1=$K</C79718O$"6<84$79$F/0$F:LJC1L109$

961$89/97897F/C$218<C98O$$

$

M0$70912E71H$3<7=1$H/8$<81=$9:$3<7=1$961$70912E71H124$/0=$:J10,10=1=$K<1897:08$

H121$/8Y1=$9:$10F:<2/31$=18F27J97E1$89/91L1098$;2:L$961$8<?[1F98O$"61$

K<1897:08$70FC<=1=$961$;:CC:H703$9:J7F8d$J12F17E1=$=132/=/97:084$H6/9$3/E1$C:H$

/0=$6736$8F:2184$961$C78910703$92/70703$/9$6:L14$=7;;1210F18$?19H110$961$9H:$91898$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )*$

V(GO+++S$/0=$>#GWIMX4$J12F1J97:0$:;$961$8F/C18$<81=$70$961$918984$C::J703$6/?7984$

/0=$J12F17E1=$8:<0=$C1E1C$?19H110$961$91898O$$

$

E;R =/$/+/#/013*3++"61$=/9/$/0/CB878$L/70CB$;:CC:H8$961$21F:LL10=/97:08$:;$961$R"#$;:2$8<F6$91898$

(ITU, 1997) (ITU, 2003)O$M==797:0/C$/0/CB878$78$/C8:$J12;:2L1=$9:$F:LJC191$961$2181/2F6$:?[1F97E18O$"61$/0/CB878$L196:=8$/21$?271;CB$=18F27?1=$70$9678$81F97:0O$

G1F97:0$.$J2181098$961$218<C98$/0=$/0/CB878O$

E;R;! <53$+!+R0$"189$+4$/0/CB878$:;$E/27/0F1$VMA]_MX$H/8$<81=O$"61$;/F9:28$70$961$L:=1C$H121$

/8$;:CC:H8d$5:=1F$q$5:=703$J2:F188$<0=12$9189$VU$C1E1C84$"/?C1$SX`$ZQF12J9$q$

G:<0=$1QF12J9$V.$C1E1C84$"/?C1$UX`$/0=$"189G791$q$G791$H6121$9189$H/8$J12;:2L1=$

V9H:$C1E1C84$c791D&G9:FY6:CLXO$"61$MA]_M$L:=1C$H796$961$=1J10=109$E/27/?C1$

G<?[1F97E1$@7;;1210F1$f2/=1$VG@fX$H/8$=1912L701=$<8703$961$;:CC:H703$1K</97:0d$

G@f$q$>1/0$N$5:=1F$N$ZQF12J9$N$"189G791$N$5:=1FjZQF12J9$N$5:=1Fj"189G791$N$

ZQF12J9j"189G791$N$5:=1FjZQF12J9j"189G791$N$Z22:2O$c:89$6:F$91898$H121$/C8:$

L/=1$9:$70E18973/91$=7;;1210F18$H79670$961$;/F9:28$5:=1F$/0=$ZQF12J9O$

M==797:0/CCB4$961$/FK<721=$=/9/$H121$<81=$9:$;70=$/$L:=1C$;:2$J12F17E1=$/<=7:$

K</C79B$/8$/$;<0F97:0$:;$?79$2/914$/88<L703$/$C701/2$21C/97:0$?19H110$96:81O$P70/CCB4$

=/9/$;2:L$"189$+$H121$L/JJ1=$/3/7089$F:2218J:0=703$=/9/$;2:L$/$J21E7:<8$9189$

H79670$Z(#4$J2:[1F9$@&@M(M$(EBU, 2009)4$9:$:?812E1$961$21C/97:0$?19H110$961$8F/C18$:;$961$(GO+++S$/0=$>#GWIM$1E/C</97:0$L196:=8O$"678$78$21J:291=$70$/$

81J/2/91$81F97:0$V.O.XO$

E;R;, <53$+,+R0$"189$)4$L1/0$E/C<18$:;$961$=7;;12109$F:0=797:08$9:319612$H796$96172$/88:F7/91=$

-.m$F:0;7=10F1$70912E/C8$H121$F/CF<C/91=O$"6181$H121$70E18973/91=$;:2$

87307;7F/09$=7;;1210F18$/0=$F:LJC7/0F1$9:$961$92/08J/210FB$F279127/$70$G1F97:0$)O$

R0$/==797:04$/$0<L?12$:;$JC/001=$F:LJ/278:08$?19H110$P>$/0=$@M(N$H121$

L/=1$?B$L1/08$:;$J/72,H781$9$918984$H6121$961$1;;1F9$:;$L<C97JC1$F:LJ/278:08$

H121$F:092:CC1=$?B$/JJCB703$961$;/C81$=78F:E12B$2/91$VP@IX$J2:F1=<21$(Benjamini & Hochberg, 1995)O$$

E;R;7 "#$5%P*5I3+"61$/0/CB878$<81=$L1/0703$F:0=108/97:0$/8$961$J27L/2B$L196:=4$H6121$C/231$

813L1098$:;$=18F27J97E1$92/08F27J98$/21$F:0=1081=$709:$86:29$<0798$:;$L1/07034$:2$

a961$18810F1b$(Kvale & Brinkmann, 2009) (Tesch, 1990)O$"61$<0798$:;$L1/0703$/21$9610$C/?1CC1=$709:$F:LL:0$961L18$;:2$/CC$;:<2$70912E71H8$/0=$9610$J2181091=$/8$

=18F27J97E1$91Q98O$

N S53(0$3+/#'+/#/013*3+

N;! <53$+!+P:2$1/F6$927/C4$961$G<?[1F97E1$@7;;1210F1$f2/=1$VG@fX$H/8$F/CF<C/91=$?B$

8<?92/F9703$961$32/=1$/887301=$9:$21;1210F1$;2:L$961$32/=1$/887301=$9:$961$:?[1F9$

<0=12$9189O$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )+$

N;!;! B&3$T3)%55#*#4+c:89,8F2110703$7=1097;718$/0=$21[1F98$8<?[1F98$96/9$86:H$/0$70/?7C79B$9:$

=78F27L70/91$961$21;1210F1$V<0J2:F1881=X$8730/C8O$"61$J2:F1=<21$;:CC:H8$961$

21F:LL10=/97:08$70$(GO+++SO$P72894$/0B$791L$96/9$21F17E1=$C:H$/E12/31$32/=1$

/F2:88$/CC$8<?[1F984$7O1O4$6/E703$/$L1/0$G@f$?1C:H$e)O*4$H/8$91LJ:2/27CB$21L:E1=$

=<2703$961$;:CC:H703$89/31O$G1F:0=4$;:2$1E12B$8<?[1F94$961$21L/70703$=/9/$96<8$

:?9/701=$H/8$8<?[1F91=$9:$/$:01,87=1=$9$9189$V!q*O*.X$9:$/88188$961$C7Y1C76::=$96/9$961$L1/0$:;$961$=78927?<97:0$;:2$1/F6$8<?[1F9$78$n12:$:2$321/912O$G<?[1F98$

;/7C703$9:$J2:=<F1$/$L1/0$96/9$78$87307;7F/09CB$C188$96/0$n12:$/9$!q*O*.$H121$21[1F91=$;2:L$961$8<?81K<109$/0/CB878O$M8$/$218<C9$:;$9678$J2:F1884$0701$8<?[1F98$

H121$21[1F91=$/0=$961$/88:F7/91=$=/9/$H121$21L:E1=$;2:L$961$=/9/$819O$W10F14$

=/9/$;2:L$)+$8<?[1F98$21L/701=$;:2$/0/CB878O$M;912$9678$/=[<89L1094$961$=/9/$819$

F:09/701=$U)*$=/9/$J:7098$V)+$8<?[1F98$j$U$F:=1F8$j$.$791L8XO$$

$

N;!;, -UVW-++"61$MA]_M$2187=</C8$H121$91891=$;:2$0:2L/C$=78927?<97:0$<8703$961$%:CL:3:2:E,

GL720:E$9189O$M8$%q*O*Ti$VJq*O+SSX4$0:2L/C$=78927?<97:0$:;$2187=</C8$F:<C=$0:9$

?1$21[1F91=$/0=$78$96121;:21$/88<L1=O$R0$/==797:04$;:2$1/F6$F:L?70/97:0$:;$F:=1F$

/0=$1QF12J94$961$=/9/$H121$F61FY1=$;:2$0:2L/C$=78927?<97:0$?B$L1/08$:;$961$

G6/J72:,^7CY$9189O$]<9$:;$961$)*$F:L?70/97:084$961$0<CC$6BJ:961878$H/8$21[1F91=$

;:2$;7E1$F/818$:0CB$V!q*O*.XO$"678$7LJC718$96/9$961$E/89$L/[:279B$:;$961$=/9/$F:L1$;2:L$/$0:2L/CCB$=78927?<91=$J:J<C/97:0O$!1E101\8$9189$:;$1K</C79B$:;$122:2$

E/27/0F18$B71C=1=$PVT-4Ti*Xq+O))l$VJq*O+l)XO$W10F14$1K</C$122:2$E/27/0F18$

F:<C=$0:9$?1$21[1F91=$/0=$H121$96121;:21$/88<L1=O$"61$1QJ127L109/C$=18730$

70FC<=1=$/$2/0=:L7n/97:0$:;$?:96$927/C$:2=12$/0=$/88730L109$:;$897L<C7$9:$961$

32/J67F/C$<812$70912;/F1$?<99:08$H79670$1/F6$927/CO$"6<8$70=1J10=10FB$?19H110$

=/9/$J:7098$H/8$/8F129/701=O$R0$8<LL/2B4$961$/88<LJ97:08$<0=12CB703$MA]_M$

H121$0:9$E7:C/91=O$"61$MA]_M$78$8<LL/2781=$70$"/?C1$iO$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ ))$

?'#3-!Z/!"UJH"!?'#3-/!

Source Type III Sum of Squares df

Mean Square F Sig. "c)

Corrected Model 421.883a 39 10.818 15.619 .000 .616 Intercept 991.737 1 991.737 1431.943 .000 .790 Codec 117.840 3 39.280 56.715 .000 .309 Excerpt 253.043 4 63.261 91.340 .000 .490 TestSite 7.724 1 7.724 11.152 .001 .029 Codec * Excerpt 12.097 12 1.008 1.456 .139 .044 Codec * TestSite 2.371 3 .790 1.141 .332 .009 Excerpt * TestSite 3.862 4 .965 1.394 .235 .014 Codec * Excerpt * TestSite 8.175 12 .681 .984 .464 .030 Error 263.181 380 .693

Total 1723.150 420

Corrected Total 685.064 419

a. R Squared = .616 (Adjusted R Squared = .576) "61$MA]_M$86:H1=$96/9$/CC$96211$L/70$;/F9:28$H121$87307;7F/09O$"61$C/23189$

1;;1F9$87n1$H/8$;:<0=$;:2$5:=1F$V"c)q*OT+X$/0=$ZQF12J9$V"c)q*OU-XO$P:2$961$21L/70703$;/F9:28$/0=$F:L?70/97:084$961$1;;1F98$H121$013C737?C1$V"c)s*O*.X4$8:$961$;/F9:28$5:=1F$/0=$ZQF12J9$H121$;<29612$70E18973/91=$/0=$8<?[1F91=$9:$J:89,

6:F$91898O$

$

N;!;7 A(0$*:05+)&6:/%*3&#3+&X+)&'5)3+"61$G@f$L1/0$E/C<18$/0=$96172$/88:F7/91=$F:0;7=10F1$70912E/C8$;:2$=7;;12109$

F:=1F8$/F2:88$/CC$1QF12J98$/21$86:H0$70$P73<21$lO$M$"<Y1B$WG@$J:89,6:F$9189$

V!q*O*.X$H/8$J12;:2L1=$9:$;70=$961$87307;7F/09$=7;;1210F18$?19H110$F:=1F8$V"/?C1$-XO$"61$218<C98$86:H1=$96/9$96121$78$/$87307;7F/09$=7;;1210F1$?19H110$961$

L1/0$G@f$;:2$1E12B$J/72,H781$F:L?70/97:0$:;$961$F:=1F8O$"61$L1/0$G@f$27818$

87307;7F/09CB$;:2$1/F6$70F21/81$70$?79$2/91O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )T$

$)*+,&-!c/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!(26-($!'(&2$$!-@(-&:%$/!

$

?'#3-!X. Multiple comparisons of codecs (Tukey HSD); mean SDGs and resulting homogenous codec subsets (CS).

Codec N Codec subset

CS1 CS2 CS3 CS4 96 kbit/s (AAC + SBR) 105 -2.303 128 kbit/s (AAC) 105 -1.820 160 kbit/s (AAC) 105 -1.313 192 kbit/s (AAC) 105 -0.852 Sig. 1.000 1.000 1.000 1.000 The error term is Mean Square (Error) = 0.693.

N;!;E A(0$*:05+)&6:/%*3&#+&X+5O)5%:$3+"61$G@f$L1/0$E/C<18$/0=$96172$/88:F7/91=$F:0;7=10F1$70912E/C8$;:2$=7;;12109$

F:=1F8$/0=$1QF12J98$/21$86:H0$70$P73<21$iO$M$"<Y1B$WG@$J:89,6:F$9189$V!q*O*.X$H/8$J12;:2L1=$9:$;70=$961$87307;7F/09$=7;;1210F18$?19H110$1QF12J98$/F2:88$F:=1F8$

V"/?C1$+*XO$"61$218<C98$86:H1=$96/9$96121$H121$96211$1QF12J9$8<?8198$VZGX$H6121$

961$1QF12J98$H79670$1/F6$8<?819$H121$0:9$87307;7F/09CB$=7;;12109$;2:L$:01$

/0:96124$?<9$=7;;121097/91=$87307;7F/09CB$;2:L$961$1QF12J98$:;$961$:9612$8<?8198O$

P:2$1/F6$8<?8194$961$F:09109$H/8$/8$;:CC:H8d$ZG+$e$GJ11F6VJ/0X$/0=$

GJ11F6VJ/0XN.Q!)`$ZG)$e$W:<81`$/0=$ZGT$e$MJJC/<81$H$/00:<0F12$/0=$5C/887F/CO$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )U$

)*+,&-!Z/!O-'4!FCM!'46!X]!d!(245*6-4(-!*4%-&1'3$!52&!(26-($!'46!-@(-&:%$/!$

?'#3-!.P. Multiple comparisons of excerpts (Tukey HSD); mean SDGs and resulting homogenous excerpt subsets (ES).

Excerpt N Excerpt subset

ES1 ES2 ES3 Speech (pan) 84 -2.554 Speech (pan) + 5xL2 84 -2.496 House 84 -1.190 Applause w announcer 84 -0.830 Classical 84 -0.790 Sig. 0.992 1.000 0.998 The error term is Mean Square (Error) = 0.693. $

N;, <53$+,+

N;,;! B&3$T3)%55#*#4+"61$J:89,8F2110703$H/8$J12;:2L1=$/FF:2=703$9:$891J$+eU$?1C:H$/0=$798$:?[1F97E1$

H/8$9:$21[1F9$8<?[1F98$96/9$H121$<0/?C1$9:$=78F27L70/91$?19H110$897L<C7$70$912L8$

:;$21;1210F1$8730/C8$/0=$/0F6:2$8730/C8O$"61$8F:21$2/031$<81=$?B$1/F6$8<?[1F9$

H/8$/C8:$70E18973/91=O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ ).$

78)5'&(&%.)%,)&3"+%&9.)#"9"#"+$")4&/+-(48)"61$J2:?/?7C79B$:;$F:221F9CB$7=1097;B703$961$67==10$21;1210F1$8730/C$?B$F6/0F1$70$:01$927/C$70FC<=703$910$8730/C8$78$*O+O$"61$

9:9/C$0<L?12$:;$927/C8$78$910O$"61$L707L<L$21K<721=$0<L?12$:;$F:221F9$

7=1097;7F/97:08$;:2$/CC$927/C84$0R@4$/9$!$q*O*+$78$F/CF<C/91=$?B$L1/08$:;$961$F<L<C/97E1$=78927?<97:0$;<0F97:0$:;$961$?70:L7/C$=78927?<97:0$8/978;B703$?VQ`$+*4$

*O+Xk*O--4$H6121$0R@$g$QO$5:081K<109CB4$/$8<?[1F9$L<89$86:H$0R@$k$.$9:$;<C;7C$9678$

F:0=797:0O$^610$/JJCB703$9678$F:0=797:04$81E10$8<?[1F98$H121$21[1F91=$;2:L$961$

8<?81K<109$/0/CB818O$

$

:8)5'&(&%.)%,)4$,#")#"9"#"+$")4&/+-(48)":$/8F129/70$96/9$8<?[1F98$J/88703$961$9189$/?:E1$=:$0:9$<0=128F:21$961$21;1210F1$8730/C84$21;1210F1$8730/C8$H6121$GF:21s-*$

H121$F:<091=O$G<?[1F98$86:H703$L:21$96/0$96211$8<F6$8F:218$H121$21[1F91=O$P7E1$

8<?[1F98$;/7C1=$9678$9189`$;:<2$:;$961L$H121$/C21/=B$21[1F91=$70$961$J21E7:<8$891JO$

W10F14$:01$/==797:0/C$8<?[1F9$H/8$21[1F91=$/8$/$218<C9$:;$9678O!!;8)5'&(&%.)%,)&3"+%&9.)-+3)4$,#")-+$<,#)4&/+-(48)":$108<21$961$/88188L109$:;$/0F6:2$8730/C84$9H:$L1/8<218$H121$<81=$;:2$1/F6$8<?[1F9d$/X$961$L1/0$E/C<1$:;$M0F6:2$

8F:218$/0=$?X$961$0<L?124$0ZMG4$:;$8F:218$1QF11=703$/$;7Q1=$8F:21$E/C<1O$"61$E/C<1$

<81=$70$?:96$L1/8<218$H/8$GF:21qS*O$M8$/$8F:21$/?:E1$9678$E/C<1$H:<C=$

21J218109$a3::=b$:2$a1QF1CC109b4$79$78$21/8:0/?C1$0:9$9:$1QJ1F9$8<F6$/$K</C79B$;:2$/$

L/[:279B$:;$M0F6:2$8730/C8O$R0$?X4$0ZMG$q.$H/8$F6:810O$^610$/JJCB703$/0B$:;$96181$

F:0=797:084$:01$8<?[1F9$;/7C1=4$?<9$H/8$/C21/=B$21[1F91=$70$891J$+$/?:E1O$

$

=8)1$,#")#-+/")*4"3)'.)4*'>"$%48)":$/FF:LL:=/91$;:2$961$21F:LL10=/97:0$:0$aC188$F2797F/Cb$/0=&:2$a9::$F2797F/Cb$8<?[1F984$961$21L/70703$8<?[1F98\$8F:218$H121$

/0/CB81=$;:2$1/F6$F:L?70/97:0$:;$F:=1F$/0=$1QF12J94$B71C=703$+**$F:L?70/97:08O$

"61$70912K</297C1$2/0314$RrI4$H/8$F/CF<C/91=$;:2$961$8F:218$:;$1/F6$F:L?70/97:0O$M$

8F:21$:<987=1$961$2/031$TRrI$;2:L$961$L1=7/0$H/8$F:087=121=$/0$1Q921L1$E/C<1O$

"61$0<L?12$:;$1Q921L1$E/C<18$J12$8<?[1F9$H/8$F61FY1=$/0=$70$0:$F/81$=7=$/0B$

8<?[1F9$86:H$/$0<L?12$1QF11=703$U$:<9$:;$961$+**$[<=31L1098$L/=1O$"678$H/8$

F:087=121=$/$E/C7=$J12;:2L/0F1O$W10F14$0:$8<?[1F98$H121$21[1F91=$:0$9678$

F279127:0$:0CBO$

$

R0$9:9/C4$17369$8<?[1F98$H121$21[1F91=$/8$/$218<C9$:;$J:89,8F2110703O$"61$=/9/$819$

0:H$F:09/701=$))**$=/9/$J:7098$V))$8<?[1F98$j$+*$8B891L8$j$+*$791L8XO$$

$

N;,;, A(0$*:05+)&6:/%*3&#3+&X+313$563+R0$9678$81F97:04$961$J12;:2L/0F1$:;$961$=7;;12109$8B891L8$/F2:88$/CC$1QF12J98$

1QF1J9$961$ZC1F92:07F$V/99X$78$86:H0$VP73<21$-XO$"61$ZC1F92:07F$V/99X$=/9/$H/8$

1QFC<=1=$;2:L$961$;73<21$?1F/<81$9678$1QF12J9$H/8$7081291=$709:$961$8B891L8$/9$/0$

/9910</91=$C1E1C$/0=$96121;:21$=7=$0:9$21;C1F9$0:2L/C$?2:/=F/89703$:J12/9703$

F:0=797:08O$"61$218<C98$;:2$9678$1QF12J9$VG1F97:0$.O)OTOUX$F:0;72L$96/9$79$H/8$

J12F17E1=$=7;;12109CB$;2:L$961$:9612$1QF12J98$?B$1Q67?79703$C:H$8F:218$:0$/CC$

8B891L8$=<1$9:$/$6736$0:781$C1E1C$/0=$96<8$H:<C=$?1$<0;/72$9:$70FC<=1O$R0$P73<21$-4$

FC1/2$=7;;1210F18$F/0$?1$8110$?19H110$961$=7;;12109$F:0=797:08O$R0$961$;:CC:H703$

81F97:084$961$=7;;1210F18$H7CC$?1$70E18973/91=$;<29612O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )S$

$

$)*+,&-!X/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!$B$%-A!'(&2$$!'33!-@(-&:%$!-@(-:%!03-(%&24*(!;'%%=/!

+

N;,;7 A(0$*:05+)&6:/%*3&#3+&X+313$56Y5O)5%:$+R0$9678$81F97:0$961$J12;:2L/0F1$:;$961$=7;;12109$8B891L8$;:2$1/F6$1QF12J9$78$

86:H0$VP73<21$+*$962:<36$P73<21$+-X$9:319612$H796$/$?271;$/0/CB878$:0$J:887?C1$

1QJC/0/97:08$9:$8B891L$J12;:2L/0F1!$$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )l$

!"#"$"% &''()*+,-./01-)223*24,5-

$)*+,&-!.PR!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!"::3',$-!7*%8!'442,4(-&/!

$

"61$:27370/C$/<=7:$H/8$8C7369CB$?/0=,C7L791=4$H67F6$L/B$?1$961$F/<81$:;$961$

87L7C/2$8F:218$/9$961$673612$?79$2/918O$R9$/C8:$6/=$7LJ<C818$96/9$F:09/701=$C/231$

/L:<098$:;$6736$;21K<10F718$96/9$H121$FC7JJ1=$70$961$P>$8B891L8O$"61$=7;;1210F1$

?19H110$P>$+$/0=$P>$)$H/8$J2:?/?CB$F/<81=$?B$961$=7;;1210F1$70$L196:=$:;$

FC7JJ703O$"61$92/=797:0/C$J21,1LJ6/878$FC7JJ703$:0$961$C1;9&27369$/<=7:$70$P>$)$

H/8$J2:?/?CB$961$F/<81$:;$798$C:H$8F:21O$R0$P>$+4$961$J21,1LJ6/878$FC7JJ703$H/8$

=:01$:0$961$L<C97JC1Q$8730/CO$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )i$

!"#"$"# 6()++/4)(-

$)*+,&-!../!O-'4!1'3,-$!'46!X]!d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!T3'$$*('3/!

$

"61$1QF12J9$F:09/701=$?2/88$70892<L1098$/0=$F/89/0198$H796$C/231$/L:<098$:;$

6736$;21K<10F718$/0=$92/0871098$FC7JJ1=$70$961$P>$8B891L8O$M8$H796$961$J21E7:<8$

1QF12J94$961$=7;;1210F1$?19H110$P>$+$/0=$P>$)$H/8$L:89$C7Y1CB$/9927?<9/?C1$9:$

961$FC7JJ703$L196:=O$$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ )-$

!"#"$"$ 7(,40532/4-

$)*+,&-!.L/!O-'4!1'3,-$!'46!X]!d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!03-(%&24*(/!

$

"61$:27370/C$/<=7:$F:09/701=$K<791$892:03$/0=$86:29$92/08710984$H67F6$L1/08$96/9$

961B$F:09/701=$/$C:9$:;$6736$;21K<10FB$F:LJ:01098O$"6181$F:LJ:01098$H121$

FC7JJ1=$?B$961$J21,1LJ6/878$FC7JJ128$70$961$P>$8B891L84$/$L196:=$96/9$J2:?/?CB$

218<C91=$70$961$C:H$8F:218O$R0$F:092/89$9:$961$9H:$J21E7:<8$1QF12J984$P>$)$

21F17E1=$/$673612$8F:214$H67F6$7LJC718$96/9$961$/291;/F98$H121$J12F17E1=$/8$C188$

81E121$;:2$9678$1QF12J9O$

$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ T*$

!"#"$"8 7(,40532/4-9)00,2*)0,:;-

$)*+,&-!.[/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!03-(%&24*(!;'%%-4,'%-6=/!

$

"678$H/8$961$8/L1$1QF12J9$/8$ZC1F92:07F$?<9$/9910</91=$?B$/==797:0/CCB$))$=($9:$

/E:7=$FC7JJ703$961$P>$8B891L8O$#0;:29<0/91CB4$961$L/Y1,<J$3/70$/JJC71=$9:$9678$

F:0=797:0$218<C91=$70$/$6736$0:781$C1E1C$96/9$F/<81=$C:H$8F:218$;:2$/CC$8B891L8O$

"61$L/70$0:781$8:<2F1$H/8$961$P>$1QF7912O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ T+$

!"#"$"! <3*+,-

$)*+,&-!.\/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!a2,$-/!

$

"678$1QF12J9$70FC<=1=$/$?/88$8B0961n7812$H796$/$F:LJC1Q$H/E1;:2L$96/9$

F:09/701=$86:29$8J7Y18$96/9$J2:?/?CB$F/<81=$961$C:H$8F:218$/9$C:H12$@M(N$?79$

2/918O$!$

$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ T)$

!"#"$"= >3'?,20-

$)*+,&-!.]/R!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!V2:b-4%/!

$

"61$1QF12J9$70FC<=1=$/$J/001=$67,6/9$96/9$L/B$6/E1$F:0927?<91=$9:$961$C:H$

8F:218$/9$961$C:H12$@M(N$?79$2/918O$ZQF1J9$;:2$961$67,6/94$961$1QF12J9$=7=$0:9$

70FC<=1$9::$L<F6$6736$;21K<10FB$10123B4$/$F:0=797:0$96/9$J2:E1=$9:$?1$

/=E/09/31:<8$;:2$P>$/8$H1CC$/8$;:2$MM5O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ TT$

!"#"$"@ >3'A3B-

$)*+,&-!.Y/!O-'4!1'3,-$!'46!X]!d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!V2:W2@/!

$

"678$1QF12J9$6/=$/$C/231$/L:<09$:;$6736$;21K<10FB$F:09109O$"61$8B891L8$

1LJC:B703$GJ1F92/C$(/0=$I1JC7F/97:04$G(I4$B71C=1=$/$673612$/<=7:$?/0=H7=964$

H67F6$H/8$L:89$0:97F1/?C1$/9$-S$Y?79&8O$$

$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ TU$

!"#"$"C D',,41E-923-')2;-

$)*+,&-!.c/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!F:--(8^!;42!:'4=/!

$

"61$;1L/C1$8J11F6$1QF12J9$H/8$21F:2=1=$70$L:0:$/9$FC:81$=789/0F1O$(1F/<81$961$

E:7F1$70$9678$1QF12J9$H/8$F10921=$V1K</C$8730/C8$70$C1;9$/0=$27369$F6/001CX4$96121$

H/8$0:$=7;;1210F1$8730/C$Vq!,IX$96/9$6/=$9:$?1$F:=1=O$W10F14$961$MM5$F:<C=$

/CC:F/91$/CC$?798$9:$961$8<L$8730/CO$"678$H/8$J2:?/?CB$961$21/8:0$;:2$961$6736$

@M(N$8F:2184$1QF1J9$;:2$Ui$Y?79&8O$$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ T.$

!"#"$"F D',,41E-9')2;-

$)*+,&-!+i/!O-'4!1'3,-$!'46!X]!d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!F:--(8^!;:'4=/!

"61$:27370/C$/<=7:$F:09/701=$:0CB$/$8703C1$;1L/C1$8J1/Y12$J/001=$9:$:01$87=1d$

961$70912F6/001C$C1E1C$=7;;1210F1$H/8$S$=(O$R0$9678$F/814$<8703$J/2/L1927F$89121:$

/9$Ui$Y?79&8$6/=$/0$/=E/09/31O$M9$673612$?79$2/9184$J/2/L1927F$89121:$H/8$0:9$

<81=4$H67F6$F/<81=$/$?C<2$70$961$89121:$7L/31O$"61$92/=797:0/C$J21,1LJ6/878$

FC7JJ703$:0$961$C1;9&27369$/<=7:$70$P>$)$H/8$J2:?/?CB$961$F/<81$:;$961$C:H$8F:21O$

R0$P>$+4$961$J21,1LJ6/878$FC7JJ703$H/8$=:01$:0$961$L<C97JC1Q$8730/CO$$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ TS$

!"#"$"%G H35(:-

$)*+,&-!.X/!O-'4!1'3,-$!'46!X]d!(245*6-4(-!*4%-&1'3$!52&!0@(-&:%!e!K2&36/!

$

P:2$961$^:2C=$1QF12J94$961$8F:218$;:CC:H1=$H6/9$L/B$?1$1QJ1F91=$;2:L$/0$

70F21/81=$?79$2/91O$

$

!"#"$"%% D*II)5J-3K-I*(0/'(,-43I')5/+32+-"61$=/9/$8<LL/2781=$70$P73<21$+*$962:<36$P73<21$+-$J2:=<F1=$961$;:CC:H703$

:?812E/97:08d$

• R0$L:89$F/8184$/9$673612$?79$2/918$0:$87307;7F/09$=7;;1210F18$H121$:?812E1=4$

7O1O4$961$F:0;7=10F1$70912E/C8$:E12C/JO$"61$>#GWIM$L196:=$L/B$?1$C188$

8108797E1$9:$961$8L/CC12$=7;;1210F18$/9$6736$?79$2/918O$

• R0$81E12/C$F/8184$96121$H/8$F:0;<87:0$?19H110$21;1210F1$/0=$F:=1=$

8730/C8O$

• 5129/70$8730/C8$H121$L:21$F2797F/C4$7O1O4$J/001=$8J11F6$/0=$92/087109$

8:<0=8$/C8:$:FF<22703$70$L:=120$L<87FO$

• >:0:9:07F/CCB$70F21/8703$8F:218$;:2$70F21/8703$?79$2/91$H121$:?812E1=$

H796$/$;1H$1QF1J97:08O$"678$H/8$J2:?/?CB$J/29CB$F/<81=$?B$961$31012/C$

70F21/81$70$/<=7:$?/0=H7=96$/0=$=1F21/81$70$K</097n/97:0$=789:297:0$96/9$

;:CC:H8$H796$673612$?79$2/91O$

• P>$+$:<9J12;:2L1=$@M(N$/9$C:H$?79$2/918O$$

• P>$)$H/8$L:21$8108797E1$9:$F129/70$1QF12J984$J2:?/?CB$=<1$9:$798$=7;;12109$

FC7JJ703$/C3:2796L$70$F:L?70/97:0$H796$798$0/22:H12$?/0=H7=96O$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ Tl$

N;7 J&6:0*/#)5+$&+/('*&+.(/0*$1+)%*$5%*/+":$217912/914$;:<2$F279127/$:;$/<=7:$K</C79B$/0=$92/08J/210FB$H121$=78F<881=$70$

G1F97:0$)O$R;$?79$92/08J/210FB$78$:L7991=4$/8$79$H/8$0:9$/7L1=$;:2$70$/0B$:;$961$

91891=$8B891L84$96211$F279127/$21L/70d$

+O J12F1J9</C$92/08J/210FB4$H67F6$L1/08$96/9$0:$89/97897F/CCB$87307;7F/09$=7;;1210F1$?19H110$961$21;1210F1$/0=$/$91891=$8B891L$86:<C=$?1$;:<0=`$

)O ?2:/=F/89$K</C79B4$H67F6$L1/08$96/9$961$L1/0$/F2:88$/CC$791L84$/0=$J21;12/?CB$/C8:$961$L1/0$:;$1/F6$791L4$86:<C=$;<C;7C$G@f$g$,+$70$(GO+++S$:2$

GF:21$g$i*$70$>#GWIM`$/0=$

TO P>$K</C79B4$H67F6$L1/08$96/9$961$91891=$8B891L$86:<C=$86:H$1K</C$K</C79B$H610$F:LJ/21=$H796$/$8J1F7;7F$P>$8B891LO$$

$

"61$F:LJC7/0F1$:;$961$91891=$8B891L8$9:$961$F279127/$H/8$70E18973/91=$/0=$96181$

70E18973/97:08$/21$=78F<881=$?1C:HO$

N;7;! B5%)5:$(/0+$%/#3:/%5#)1+/#'+2%&/')/3$+.(/0*$1+R0$"189$+4$0:01$:;$961$8B891L8$H/8$;:<0=$9:$?1$J12F1J9</CCB$92/08J/2109O$P:2$961$

?79$2/918$-S4$+)i4$/0=$+S*$Y?79&84$0:01$:;$961$91891=$791L8$;<C;7CC1=$9678$F279127:0O$

M9$+-)$Y?79&84$961$791L8$aMJJC/<81$H$/00:<0F12b$/0=$961$9H:$8J11F6$791L8$=7=$

0:9$21/F6$J12F1J9</C$92/08J/210FBO$"61$?2:/=F/89$K</C79B$F279127:0$H/8$;<C;7CC1=$

70$"189$+$:0CB$/9$+-)$Y?79&84$?<9$870F1$J/001=$8J11F6$/9$9678$?79$2/91$=7=$0:9$21/F6$

/$G@f$g$,+4$9678$8B891L$86:<C=$?1$<81=$H796$F/<97:0O$P:2$"189$)4$/$87L7C/2$/0/CB878$

H/8$L/=1$V"/?C1$++XO$

$

?'#3-!../!T2A:3*'4(-!%2!`,'3*%B!(&*%-&*24!.!'46!L!52&!%8-!6*55-&-4%!$B$%-A$!*4!?-$%!L/!

!! 8/+-4,)+0)!4+3)19$&!/-!1)-)1)+0)!/9)1&$%(!0/+-4,)+0)!4+3)19$&!/-!43).:!;8143)14/+!<=!

!! >)$+!4(!/9)1!?@:!;8143)14/+!A=!

!! B/35!0143)14$!$1)!-'&-4&&),:!

!! C/+)!/-!35)!0143)14$!$1)!-'&-4&&),:!$

!=->?+ @A+

CO)5%:$+ EZ+ QE+ [Q+ !,Z+ !Q\+ ![,+ !+ ,+

D%%&$'()!7!$++/'+0)1! !! !! !! !! !! !! !! !!8&$((40$&! !! !! !! !! !! !! !! !!E&)031/+40! !! !! !! !! !! !! !! !!E&)031/+40!;$33=! !! !! !! !! !! !! !! !!F/'()! !! !! !! !! !! !! !! !!G/%H)+3! !! !! !! !! !! !! !! !!G/%I/J! !! !! !! !! !! !! !! !!6%))05K!;+/!%$+=! !! !! !! !! !! !! !! !!6%))05K!;%$+=! !! !! !! !! !! !! !! !!L/1&,! !! !! !! !! !! !! !! !!>)$+!9$&')!-/1!(M(3).(N! !! !! !! !! !! !! !! !!N!E&)031/+40!;$33=!)J0&',),!$

!"#$

%%!&'($ $ )*+),*-,+.$

$ Ti$

M8$78$86:H0$?B$961$6:27n:09/C$C7018$70$961$9/?C14$0:01$:;$961$91891=$8B891L8$H121$

;<CCB$J12F1J9</CCB$92/08J/2109O$M9$+-)$Y?79&84$/CC$91891=$791L8$1QF1J9$ZC1F92:07F$

V/99X$/0=$J/001=$8J11F64$6:H1E124$;<C;7CC1=$9678$F279127:0O$M8$L1097:01=$

J21E7:<8CB4$961$ZC1F92:07F$V/99X$1QF12J9$=:18$0:9$21;C1F9$0:2L/C$:J12/9703$

F:0=797:08$/0=$86:<C=$?1$=78213/2=1=4$H6121/8$J/001=$8J11F6$78$F:LL:0CB$

<81=$70$?2:/=F/89703$/0=$96121;:21$86:<C=$?1$F:087=121=$/0$7LJ:29/09$1QF12J9O$

$

"61$E1297F/C$C7018$70$"/?C1$++$86:H$96/9$?:96$+-)$Y?79&8$/0=$P>$+$70$"189$)$

;<C;7CC1=$961$?2:/=F/89$K</C79B$F279127:0O$"61$/E12/31$8F:21$/F2:88$791L8$;:2$96181$

8B891L8$H/8$/?:E1$i*4$?<9$870F1$8:L1$70=7E7=</C$791L8$8F:21$C188$96/0$i*4$96181$

8B891L8$86:<C=$?1$<81=$H796$F/21$/FF:2=703$9:$961$=78F<887:0$:0$?2:/=F/89$

K</C79B$70$G1F97:0$)O$

N;7;, @A+.(/0*$1+":$9189$8J1F7;7F/CCB$961$=7;;1210F1$?19H110$961$8B891L8$@M(N$/0=$P>4$J/721=$

9H:,9/7C1=$9$91898$V=;q)+X$H121$J12;:2L1=$:0$961$L1/0$=7;;1210F1$!$?19H110$961$8F:218$;:2$@M(N$/0=$P>+$;:2$1/F6$:;$961$?79$2/918$:;$@M(N$8B891L8$/0=$1QF12J98$

1QF1J9$ZC1F92:07F$V/99X4$3qGF:21V@M(N4$t?79$2/914$1QF12J9uX,GF:21VP>$+4$t1QF12J9uXO$"678$H/8$21J1/91=$;:2$P>$)$21JC/F703$P>$+O$"61$218<C9703$J$E/C<18$

;2:L$/CC$9$91898$H121$8<?[1F91=$9:$961$P@I$J2:F1=<21$;:2$=1FC/2703$87307;7F/09$

=7;;1210F18$?/81=$:0$vq*O*.$(Benjamini & Hochberg, 1995)O$$$

P:2$P>$+$V"/?C1$+)X4$961$218<C98$86:H1=$96/9$70$:0CB$9H:$:<9$:;$961$0701$F/818$/9$

+-)$Y?79&8$961$@M(N$8B891L$H/8$J12F17E1=$/8$87307;7F/09CB$?199124$H6121/8$/9$

C:H12$?79$2/918$179612$0:$=7;;1210F1$:2$H:281$J12;:2L/0F1$?B$961$@M(N$H/8$

0:91=O$P:2$P>$)$V"/?C1$+TX4$961$218<C98$H121$L:21$=78J1281=d$/9$673612$?79$2/9184$

@M(N$8<2J/881=$P>$)`$/9$C:H12$?79$2/9184$@M(N$;1CC$?1C:H`$/0=$70$/$0<L?12$:;$

F/8184$0:$87307;7F/09$=7;;1210F18$H121$;:<0=O$"6121$H121$7089/0F18$H6121$@M(N$

H/8$8<J127:2$9:$P>$)$=:H0$9:$SU$Y?79&8$?<9$/C8:$70;127:2$<J$9:$+)i$Y?79&8O$

$

?'#3-!.L/!%!%-$%$!25!6*55-&-4(-$!#-%7--4!C"DE!'46!)O!.!52&!-@(-&:%$!-@(-:%!03-(%&24*(!;'%%=/!F*+4*5*('4%!6*55-&-4(-$!'&-!6-42%-6!#B!fEg!52&!'!8*+8-&!$(2&-!;fIf!52&!'!327-&=!25!C"DE!$B$%-A$/!

DAB+ systems at different bit rates compared with FM 1

(kbit/s) Excerpt 48 64 96 128 160 192 Applause w announcer - - - - - Classical - - - Electronic - - - + House - - - + PopKent - - - - PopRox - - - SpeechL(no pan) - SpeechL(pan) - - - - - World - - $

!"#$

%%!&'($ $ )*+),*-,+.$

$ T-$

?'#3-!.[/!%!%-$%$!25!6*55-&-4(-$!#-%7--4!C"DE!'46!)O!L!52&!-@(-&:%$!-@(-:%!03-(%&24*(!;'%%=/!F*+4*5*('4%!6*55-&-4(-$!'&-!6-42%-6!#B!fEg!52&!'!8*+8-&!$(2&-!;fIf!52&!'!327-&=!25!C"DE!$B$%-A$/!

DAB+ systems at different bit rates compared with FM 2

(kbit/s) Excerpt 48 64 96 128 160 192 Applause w announcer - + + + + Classical + + + + + Electronic - - - + House - - - + PopKent - - - -

PopRox - + SpeechL(no pan) + + + + + SpeechL(pan) + - - - + World - - -

$

N;7;7 >*$+%/$53+%5.(*%5'+X&%+$%/#3:/%5#)1+":$J21=7F9$961$?79$2/91$01F188/2B$9:$/99/70$G@fq*$V7O1O4$7LJ12F1J97?C1$=7;;1210F1$

;2:L$961$21;1210F1X4$/$L:=1C$;:2$G@f$/8$/$;<0F97:0$:;$?79$2/914$?@4$70$961$;:2L$:;$/$C701/2$F<2E14$G@f$q$'?@$N$$4$H/8$;7991=$:09:$961$=/9/$70$"189$+O$M8$961$1QF12J98$;:2L1=$961$96211$8<?8198$V811$G1F97:0$.O+OUX$ZG+$VGJ11F6!$1QF12J98X4$ZG)$VW:<81$

1QF12J9X$/0=$ZGT$VMJJC/<81$/0=$5C/887F/C$1QF12J98X4$96181$H121$921/91=$

70=7E7=</CCBO$"61$218<C9703$J/2/L19128$V'$/0=$$X4$961$3::=0188,:;,;79$VI)X4$/8$H1CC$/8$961$21K<721=$?79$2/91$;:2$;<C;7CC703$G@fq*$H/8$F/CF<C/91=$;:2$1/F6$:;$961$8<?8198$

V"/?C1$+UXO$

$

?'#3-!.\/!V'&'A-%-&$!25!%8-!3*4-'&!A26-3!FCM!e!!"#$%!'46!&-`,*&-6!#*%!&'%-!52&!FCMeP/!

ZQF12J9$

8<?819$

') $) I)$ ?@$;:2$G@fq*$tY?79&8u$

ZG+$ *O*+i$ ,.O+T$ *OUT$ )i.$

ZG)$ *O*+-$ ,TO-T$ *OT.$ )*l$

ZGT$ *O*+*$ ,)OT*$ *O+S$ )T*$

$

"61$218<C98$86:H1=$96/9$961$C701/2$L:=1C$J:7091=$9:H/2=8$01F188/2B$?79$2/918$

/?:E1$)**$Y?79&8$;:2$/0B$:;$961$8<?8198$9:$21/F6$G@f$q$*O$"61$6736189$?79$2/91$

21K<721=$H/8$;:<0=$;:2$8<?819$ZG+4$H67F6$F:09/701=$961$L:89$F2797F/C$791L84$

H6121$G@f$q$*$H/8$21/F61=$/9$I($q$)i.$Y?79&8O$"678$8<?819$/C8:$B71C=1=$961$?189$

;79$VI)q*OUTX$:;$961$L:=1CO$

$$

N;E "#$5%P*5I3+"61$218<C9703$961L18$;2:L$961$70912E71H8$70FC<=1=$70;:2L/97:0$/?:<9$961$

J12F17E1=$=132/=/97:084$H67F6$:;$961L$H121$J2:L701094$H619612$961B$H121$

1/8B&6/2=$9:$J12F17E14$/0=$H6/9$=132/=/97:08$3/E1$C:H&6736$8F:218$=<2703$961$

91898O$"61$/0/CB878$J2:=<F1=$961$;:CC:H703$=18F27J97E1$91Q9O$

$

"61$J12F17E1=$=132/=/97:08$H121$F6/0318$70$961$;21K<10FB$218J:0814$F:L?$;7C912$

1;;1F984$C:H$?79$2/91$/291;/F984$F6/0318$70$961$89121:$7L/314$C:0312$/99/FY$97L1$:;$

92/08710984$L:21$0:7814$/0=$961$=132/=/97:0$:;$961$a;11C703b$:;$961$8:<0=O$"61$

L:89$J2:L70109$=132/=/97:08$H121$F6/0318$70$961$89121:$7L/31$/0=$F6/0318$70$

!"#$

%%!&'($ $ )*+),*-,+.$

$ U*$

961$;21K<10FB$218J:081O$@132/=/97:08$F/<81=$?B$0:781$H121$1/8B$9:$J12F17E14$1O3O$

31012/C$0:781$C1E1C84$89/97F$0:781$70$87C109$J/298$:;$961$897L<C7O$MC8:$F6/0318$70$961$

89121:$7L/31$H121$J2:L70109O$$@132/=/97:08$H121$J12F17E1=$H610$961$E:7F1\8$

89121:$J/0:2/L/$J:8797:0$H/8$:;;,F109214$/8$H1CC$/8$H610$J/001=$92/0871098$

:FF<221=O$

$

@132/=/97:08$H121$6/2=$9:$J12F17E1$70$F:LJC1Q$897L<C7$96/9$=7=$0:9$F:09/70$/0B$

1/87CB$=78F120/?C1$21;1210F1$J:7098O$G:L1$8<?[1F98$;:F<81=$:0$961$31012/C$/<=7:$

K</C79B4$70FC<=703$C78910703$9:$961$/99/FY$:;$961$92/0871098$/0=$89121:$7L/31$9:$

=78F120$961$K</C79B$:;$96/9$897L<C<8O$^610$=78927?<9703$6736$8F:2184$961$8<?[1F98$

C789101=$;:2$961$J21810F1$:;$/$8:<0=`$:01$C7891012$/88:F7/91=$9678$H796$C78910703$9:$

/$L7Q$V/8$7;$/0$/<=7:$10370112XO$M$8:29$:;$H1736703$892/913B$H/8$/C8:$1LJC:B1=$

H6121$961$8<?[1F98$C789101=$;:2$961$9:9/C$K</C79B$:;$961$=132/=/97:08$9:$=78F120$

H619612$961B$H121$/FF1J9/?C1$:2$H619612$961$897L<C7$F:09/701=$:9612$

/=E/09/318O$$

$

G1E12/C$=132/=/97:08$H121$J12F17E1=$/8$/FF1J9/?C14$1O3O4$/$C:H$/L:<09$:;$

/291;/F98$70=<F1=$?B$961$F:=703$/C3:2796L$/0=$C:88$:;$/99/FY$70$961$92/0871098O$

]01$C7891012$1E10$89/91=$96/9$C:8818$:;$6736$;21K<10F718$/21$L:21$/FF1J9/?C1$

F:LJ/21=$9:$F:=703$/291;/F984$K</097n703$0:7814$/0=$C:H$?79$=1J96O$$

$

!:H$8F:218$H121$37E10$H610$961$;:CC:H703$F:0=797:08$H121$L19d$$

• /$C/231$=7;;1210F1$?19H110$961$897L<C<8$/0=$961$21;1210F1`$

• =132/=/97:08$H121$/<=7?C1$70$F:L?70/97:0$H796$6:H$L<F6$79$/;;1F91=$961$

218<C9`$1O3O4$8J11F6$H/8$0:9$8F:21=$961$C:H189$?1F/<81$961$70;:2L/97:0$70$

961$8J11F6$897CC$F/L1$962:<36$9:$961$C7891012`$$

• 961$?/0=H7=96$H/8$1Q921L1CB$C7L791=$/0=$H610$961$?/0=H7=96$F6/031=$

H796$961$8730/C`$/0=$$

• C:88$:;$6736$;21K<10F7184$6736$;21K<10FB$9:0184$/0=$C:88$:;$/99/FY$:;$961$

92/0871098$H121$J12F17E1=O$$

$

N;N S50/$*&#+25$I55#+>G;!!!Q+/#'+A]GKS-+3)/053+":$;70=$961$21C/97:0$?19H110$961$8F/C18$:;$961$(GO+++S$/0=$>#GWIM$1E/C</97:0$

L196:=84$=/9/$;:2$+)$F:0=797:08$;2:L$"189$+$H121$L/JJ1=$/3/7089$961$8/L1$

F:0=797:08$;2:L$/$J21E7:<8$Z(#$9189$(EBU, 2009)O$"61$E/27/?C18$H121$f2/=14$A$V;2:L$(GO+++SX4$/0=$GF:214$1$V;2:L$>#GWIMXO$5<2E1$;798$;:2$/$C701/2$21C/97:0$/8$H1CC$/8$:9612$;<0F97:08$H121$J12;:2L1=O$"61$C701/2$F<2E1$;79$VI)q*O-TX$218<C91=$

70$! ! !!!"#! ! !!!"#O$"61$?189$0:0,C701/2$;79$VI)q*O-UX$H/8$961$J:H12$;<0F97:0$! ! !!!""!!!!"#O$$

"61$L:=1C8$70=7F/91=$/$C701/2$:2$FC:81$9:$C701/2$21C/97:0867J$H796$0:$:2$8L/CC$

:;;819$;2:L$961$:27370$VP73<21$)*XO$R9$86:<C=$?1$0:91=$96/9$70$(GO+++S4$f2/=1$78$0:9$

=1;701=$?1C:H$+O*4$H67F6$/FF:2=703$9:$96181$L:=1C8$H:<C=$L1/0$96/9$K</C79718$/9$

961$C:H189$J/298$:;$961$>#GWIM$8F/C14$?1C:H$GF:21$w$)*4$F:2218J:0=703$9:$a?/=$

K</C79Bb4$/21$0:9$J:887?C1$9:$21J218109$:0$961$(GO+++S$8F/C1O$$

$

!"#$

%%!&'($ $ )*+),*-,+.$

$ U+$

$)*+,&-!LP/!W-3'%*24!#-%7--4!DF/...Y!M&'6-!5&2A!?-$%!.!'46!O_FaW"!F(2&-!5&2A!%8-!0D_!%-$%!DVU!PX\!(EBU, 2009)!52&!%8-!$'A-!*%-A$/!

Q =*3)(33*&#++

Q;! -('*&+.(/0*$1+/#'+$%/#3:/%5#)1+5:087=12703$961$218<C98$:;$961$1QJ127L1098$70$21C/97:0$9:$961$92/08J/210FB$

F279127/$J2181091=$70$G1F97:0$)4$81E12/C$:?812E/97:08$F/0$?1$L/=1O$$

$

"61$J21;1221=$8<?F6/001C$?79$2/91$;:2$961$791L8$70$"189$)$92/08L7991=$:E12$@M(N$

86:<C=$?1$/9$C1/89$+-)$Y?79&8$;:2$a?2:/=F/89$K</C79Bb$V/8$8703C1$791L8$?1C:H$

GF:21qi*$H121$/CC:H1=XO$P:2$aJ12F1J9</C$92/08J/210FBb4$1E10$673612$?79$2/918$

H:<C=$?1$21K<721=$=<1$9:$961$C/FY$:;$F:LJC7/0F1$:;$961$GJ11F6!VJ/0X$791LO$G<F6$

?79$2/918$H:<C=$J:81$/$J2:?C1L$/8$@M(N$F<22109CB$:0CB$/CC:H8$;:2$/$L/Q7L<L$:;$

+-)$Y?79&8O$

$

R;$961$218<C98$:;$"189$+$/21$F:087=121=$/0=$961$=1;70797:0$;:2$?2:/=F/89$K</C79B$78$

/JJC71=4$+-)$Y?79&8$H:<C=$?1$21K<721=4$H67F6$8<JJ:298$961$;70=7038$/?:E1O$G97CC$79$

6/8$9:$?1$0:91=$96/9$961$J/001=$8J11F6$1QF12J98$=7=$0:9$21/F6$/$8<;;7F7109$C1E1C$:;$

K</C79BO$R;$961$J12F1J9</C$92/08J/210FB$F279127:0$78$/JJC71=$/0=$961$C701/2$L:=1C$

70$.OT$78$<81=4$9678$J:7098$9:H/2=8$/$01F188/2B$?79$2/91$/?:E1$)**$Y?79&84$/0=$;:2$

961$L:89$F2797F/C$791L8$VJ/001=$8J11F6X4$/$?79$2/91$FC:81$9:$T**$Y?79&8$V"/?C1$+U$

70=7F/918$/9$C1/89$)i.$Y?79&8X$H:<C=$?1$011=1=O$R9$6/8$9:$?1$J:7091=$:<9$96/9$961$

1Q92/J:C/97:0$?B$L1/08$:;$/$C701/2$;<0F97:0$L/B$F:09/70$122:28$96/9$F/0$/;;1F9$961$

!"#$

%%!&'($ $ )*+),*-,+.$

$ U)$

/FF<2/FB$:;$961$J21=7F91=$01F188/2B$?792/91O$W:H1E124$96121$78$/$011=$;:2$?79$2/918$

9:$?1$H1CC$/?:E1$+-)$Y?79&84$18J1F7/CCB$;:2$F2797F/C$791L8O$

$

R0$"189$)4$7;$961$:FF<2210F1$:;$/$87307;7F/09$013/97E1$=7;;1210F1$?19H110$@M(N$/0=$

P>$;:2$/0B$791L$H/8$9:$?1$/JJC71=$/8$/$F279127:0$;:2$0:0,92/08J/210FB$:;$@M(N$70$

21C/97:0$9:$P>4$961$;:CC:H703$H:<C=$/JJCBd$/$@M(N$?79$2/91$:;$+-)$Y?79&8$H:<C=$

37E1$/$218<C9$96/9$78$/$F:LJ/2/?C1$9:$:2$?19912$96/0$/$L:=120$P>$8B891L$VP>$+X4$

H6121/8$/$?79$2/91$:;$+S*$Y?79&8$78$C7Y1CB$9:$J12;:2L$F:LJ/2/?C1$9:$:2$?19912$96/0$

961$/E12/31$9BJ18$:;$P>$92/08L799128$<81=$?B$GH1=786$I/=7:$VP>$)XO$!:H12$?79$

2/918$H:<C=$37E1$2781$9:$87307;7F/09$=132/=/97:08$:;$961$/<=7:$K</C79B$V"/?C1$+)$

/0=$"/?C1$+TXO$$

$

5129/70$9BJ18$:;$8:<0=8$/JJ1/2$K<791$F:LL:0CB$70$213<C/2$J2:32/LL7034$1O3O4$

/JJC/<81$/0=$J/001=$8J11F6$70$2/=7:O$"61$K</C79B$:;$8<F6$8:<0=8$H7CC$6/E1$/$

87307;7F/09$H17369$:0$961$:E12/CC$J12F17E1=$K</C79B$:;$/$?2:/=F/89703$F6/001C$/0=$

H7CC$96121;:21$?1$1881097/C$9:$70FC<=1$70$K</C79B$91898O$"61$:FF<2210F1$:;$8<F6$

8730/C8$70$/$9189$78$/C8:$7LJ:29/09$9:$F:087=12$H610$/E12/3703$8F:218$/F2:88$791L8$

/8$218<C98$;2:L$:01$K<791$F2797F/C$791L$L/B$?1$L/8Y1=$?B$C188$F2797F/C$:018O$M0B$

21J218109/97:0$:;$=/9/$;2:L$/$C78910703$9189$96/9$[<89$86:H8$961$/E12/31$

J12;:2L/0F1$/F2:88$791L8$L/B$278Y$L788703$8:L1$:;$961$K</C79B$=1;7F710F718$:;$

961$8B891L8O$]01$1Q/LJC1$78$=1J7F91=$70$P73<21$-$H6121$@M(N$/9$+)i$Y?79&8$

/JJ1/28$9:$?1$?19912$96/0$P>$)$:0$/E12/31O$W:H1E124$961$/0/CB878$:;$70=7E7=</C$

791L8$V"/?C1$+TX$86:H8$96/9$9H:$791L8$6/E1$87307;7F/09CB$70;127:2$K</C79BO$$

MC96:<36$961$;70=7038$70$961$F<22109$89<=B$H121$?/81=$:0$/$E12B$C7L791=$0<L?12$

:;$1QF12J984$961B$86:H$96/9$8:<0=8$96/9$/21$F2797F/C$1Q789$/8$961B$21E1/C1=$

H1/Y018818$:;$961$8B891L8$<0=12$9189O$$

$

M$H1CC,Y0:H0$J610:L10:0$78$96/9$/$F:=1F$L/B$6/E1$J12;:2L1=$H1CC$;:2$/$E/89$

0<L?12$:;$8:<0=$1QF12J98$:E12$97L14$?<9$H610$1QJ:81=$9:$/$J21E7:<8CB$01E12$

10F:<09121=$1QF12J94$79$L/B$J2:=<F1$FC1/2CB$/<=7?C1$/291;/F98$/9$961$81C1F91=$?79$

2/91O$":$21=<F1$8<F6$278Y8$H610$/$8B891L\8$?79$2/91$78$9:$?1$189/?C7861=$/0=$9:$

/FF:LL:=/91$;:2$;<9<21$J:887?C1$F2797F/C$791L84$:01$8:C<97:0$L/B$?1$9:$/JJCB$/$

8/;19B$L/2370$70$961$;:2L$:;$/$?79$2/91$96/9$78$673612$96/0$961$:01$961$F<22109$

C78910703$9189$70=7F/918$/8$?1703$01F188/2BO$

$

MC96:<36$961$1QJ127L109/C$=18730$H/8$/7L1=$9:$1L<C/91$21/C7897F$?2:/=F/89703$

F:0=797:084$79$86:<C=$?1$0:91=$96/9$961$1QJ127L1098$H121$J12;:2L1=$<0=12$L:21$

;/E:<2/?C1$F:0=797:08$96/0$/21$0:2L/CCB$;:<0=$70$21/C,C7;1$?2:/=F/89703O$]01$

1Q/LJC1$78$96/9$L:?7C1$21F1J97:0$:E12$/$C:03$=789/0F1$;2:L$961$92/08L799128$L/B$

=132/=1$961$/<=7:$70$E/27:<8$H/B84$?:96$;:2$P>$/0=$@M(NO$M0:9612$1Q/LJC1$78$

961$F:LL:0$<81$:;$F/8F/=1=$F:=1F84$18J1F7/CCB$7;$=7;;12109$?79$2/918$/21$<81=$/9$

=7;;12109$89/318$:;$961$F/8F/=1O$MC8:4$/0F7CC/2B$=/9/$L/B$32/=</CCB$<81$L:21$

?/0=H7=96$/0=$96<8$70F21/81$:E12$97L14$H67F6$L/B$70$21/C79B$C1/=$9:$/$21=<F97:0$

:;$961$/E/7C/?C1$/<=7:$?79$2/91O$$

$

R9$86:<C=$/C8:$?1$0:91=$96/9$961$@M(N$/0=$P>$J2:F18818$H121$C7L791=$9:$/$

218:C<97:0$:;$+S$?798$/9$961$97L1$:;$961$1QJ127L10984$H6121/8$8B891L8$H796$673612$

218:C<97:0$/21$0:H$/E/7C/?C1O$M0:9612$:?812E/97:0$/?:<9$C78910703$91898$70$

!"#$

%%!&'($ $ )*+),*-,+.$

$ UT$

31012/C$78$96/9$8:L1$:;$961$21;1210F1$8730/C8$897CC$70$<81$L/B$6/E1$?110$21F:2=1=$

962:<36$1K<7JL109$96/9$0:H$6/8$?110$8<2J/881=$70$912L8$:;$K</C79BO$$

$

M8$0:91=$J21E7:<8CB$70$9678$J/J124$70$F:LJ/278:0$H796$/$0<L?12$:;$:9612$L1=7/$

=78927?<97:0$;:2L/984$961$?79$2/918$F<22109CB$<81=$;:2$@M(N$/21$31012/CCB$C:H12O$

M0$:?E7:<8$F:0FC<87:0$78$96/9$@M(N$C78910128$F:<C=$J12F17E1$961$8B891L$/8$

70;127:2$9:$:9612$F:091LJ:2/2B$/<=7:$/JJC7F/97:08O$

$

MC9:319612$961$1QJ127L109/C$218<C98$7LJCB$96/9$1E10$673612$?79$2/918$L/B$?1$

011=1=$9:$21/F6$961$=18721=$C1E1C$:;$K</C79B$/0=$92/08J/210FB$70$;<9<21$

?2:/=F/89703$8B891L8O$

$

Q;, A*3)500/#5&(3+&235%P/$*&#3+P2:L$961$70912E71H84$/$0<L?12$:;$;1/9<218$7LJ:29/09$;:2$961$8<?[1F98\$

/88188L109$:;$K</C79B$H121$:?812E1=O$R0$/==797:0$9:$89/91L1098$:0$H1736703$

F:=703$/291;/F98$/3/7089$21=<F1=$/<=7:$?/0=H7=964$81E12/C$=19/7C8$H121$21J:291=O$

]01$J/297F<C/2CB$70912189703$=19/7C$H/8$21C/91=$9:$961$/JJ1/2/0F1$:;$/291;/F98$/9$

89121:$J/0:2/L/$J:8797:08$813213/91=$;2:L$961$E:7F1\8$J:8797:0O$]9612$/8J1F98$

:<987=1$961$8F:J1$:;$9678$J/J12$H121$/C8:$:?812E1=4$1O3O4$F:LJ/278:0$/0=$

1E/C</97:0$892/913718$<81=$?B$961$C78910128$/8$H1CC$/8$6:H$961$8F/C18$<81=$H121$

J12F17E1=O$]?812E/97:08$H121$/C8:$L/=1$:0$6:H$1;;1F97E1$961$92/70703$J2:F188$/9$

6:L1$H/8$8110$;2:L$961$8<?[1F98\$J:709$:;$E71H$/8$H1CC$/8$:0$961$892/913718$961$

8<?[1F98$<81=$9:$2/0Y$961$1QF12J98O$>:21$=19/7C8$:0$96181$;70=7038$H7CC$?1$

J2181091=$70$961$;<9<21O$

$

"61$1QF12J9$ZC1F92:07F$V/99X$H/8$/9910</91=$8:$/8$0:9$9:$F/<81$FC7JJ703$70$961$J21,

1LJ6/878$F72F<79O$"61$7=1/$H/8$9:$F:LJ/21$961$1QF12J9$H796$798$<0,/9910</91=$

F:<0912J/29$VZC1F92:07FXO$W:H1E124$=<1$9:$961$C:H$8730/C$C1E1C$/0=$961$;:CC:H703$

L/Y1,<J$3/704$961$0:781$C1E1C$?1F/L1$8:$C:<=$96/9$961$0:781$7981C;$F/<369$961$

C78910128\$/991097:04$H67F6$C1=$9:$87307;7F/09CB$C:H12$8F:218$;:2$961$/9910</91=$

1QF12J9O$"678$=7=$0:9$F:2218J:0=$9:$0:2L/C$?2:/=F/89703$:J12/9703$F:0=797:08O$

M==797:0/CCB4$8:L1$:;$961$J:887?C1$/291;/F98$L/B$6/E1$?110$L/8Y1=$?B$961$0:781O$

5:081K<109CB4$961$70910=1=$F:LJ/278:0$H/8$0:9$J:887?C1$/0=$6/=$9:$?1$

/?/0=:01=O$$

$

R0$"189$)4$961$=78927?<97:0$:;$8F:218$H/8$=7;;12109$?19H110$1QF12J98O$R0$8:L1$

F/8184$C/231$K</C79B$=7;;1210F18$H121$;:<0=`$;:2$:9612$F/8184$961$:JJ:8791$H/8$

:?812E1=O$^610$961$=7;;1210F18$/21$8L/CC$=<1$9:$/$J12F17E1=$6736$K</C79B$:;$961$

791L84$961$218:C<97:0$:;$961$>#GWIM$L196:=$L/B$?1$708<;;7F7109O$]0$961$:9612$

6/0=4$91898$H6121$F:=1F8$/21$F:LJ/21=$70=721F9CB$?B$L1/08$:;$/$21;1210F14$8<F6$

/8$961$(GO+++S4$H:<C=$218<C9$70$J2:?C1L8$H6121$791L8$6/E1$C/231$J12F1J9</C$

=7;;1210F18$(Soulodre & Lavoie, 1999)O$$$

$aP>$K</C79Bb$6/8$?110$<81=$70$/$H7=1$2/031$:;$L1/07038$?B$961$/<=7:$

F:LL<079BO$R0$9678$1QJ127L1094$L1/8<218$H121$9/Y10$9:$108<21$/$6736,K</C79B$

J12;:2L/0F1$H79670$961$J:887?C1$C7L798O$"678$H/8$/FF:LJC7861=$?B$1LJC:BL109$

:;$961$21F:LL10=/97:08$R"#,I$(GOU+),-$213/2=703$961$/E12/31$L<C97JC1Q$J:H12$

!"#$

%%!&'($ $ )*+),*-,+.$

$ UU$

=1E7/97:0$/8$H1CC$/8$961$J1/Y$=1E7/97:0$/0=$Z(#$I,+)i$213/2=703$/<=7:$C1E1C8$

(ITU, 1998) (EBU, 2010)O$$

M8$=78F<881=$70$TO)OU4$?/0=$C7L791=$/0F6:2$8730/C8$/21$0:2L/CCB$<81=$70$>#GWIM$

91898O$"BJ7F/CCB4$961$/0F6:2$78$8F:21=$K<791$C:H$H67C1$961$:9612$1QF12J98$21F17E1$

8F:218$96/9$/21$21C/97E1CB$6736$70$F:LJ/278:0$9:$961$/0F6:2$=<1$9:$961$/0F6:2\8$

C/231$J12F1J9</C$=7;;1210F1$;2:L$961$H7=1,?/0=$791L8O$R0$9678$1QJ127L1094$961$<81$

:;$/$H7=1,?/0=$;21K<10FB$8J1F92<L$/0F6:2$21L:E1=$8:L1$:;$96181$1;;1F98$;:2$

81E12/C$791L8O$$

$

"61$21C/97:0$?19H110$961$(GO+++S$/0=$961$>#GWIM$1E/C</97:0$8F/C18$1Q67?798$/$

C701/2$:2$FC:81$9:$C701/2$?16/E7:<2$;:2$961$70E18973/91=$F:0=797:08O$MC96:<36$8<F6$

/$21C/97:0$78$;/2$;2:L$892/7369;:2H/2=4$79$37E18$8:L1$8<JJ:29$9:$961$8:L197L18$

=78F<881=$87L7C/279B$?19H110$f2/=1qU$V:2$G@fq,+X$/0=$GF:21qi*O$"6121$/21$

788<18$21C/91=$9:$8F/C1$F:LJ21887:04$F17C703$1;;1F984$=7;;12109$?7/8184$19FO$96/9$6/E1$

9:$?1$70E18973/91=$;<29612$?1;:21$8<F6$87L7C/279718$/21$/=:J91=O$

$

Q;7 @($(%5+%535/%)F+"61$K</C79B$788<18$70$?2:/=F/89703$/21$0<L12:<8$/0=$;<29612$2181/2F6$78$011=1=$

70$81E12/C$/21/8O$G:L1$:;$961L$/21$8<LL/27n1=$70$9678$81F97:0O$

$

M0/C:3<1$P>$78$C7Y1CB$9:$?1$<81=$;:2$L/0B$L:21$B1/28$70$81E12/C$F:<092718O$

P<29612$=1E1C:JL1098$70$C:<=0188$/C730L109$/0=$F:092:C$:;$961$P>$=1E7/97:0$

9:319612$H796$01H$92/08L79912$1K<7JL109$/21$1QJ1F91=$9:$7LJ2:E1$961$P>$

K</C79B$J12;:2L/0F1$1E10$;<29612 (EBU, 2010)$(ITU, 1998)O$"678$H7CC$2170;:2F1$P>$/8$/$=1,;/F9:$/0F6:2$;:2$/<=7:$?2:/=F/89703$K</C79B$96/9$8<?81K<109$8B891L8$H7CC$

?1$F:LJ/21=$H7964$H67F6$70$798$9<20$J<98$1E10$L:21$K</C79B$J2188<21$:0$96181$

8B891L8O$"678$;/F9$70$F:L?70/97:0$H796$961$7092:=<F97:0$:;$7LJ2:E1=$21F17E128$

?:96$;:2$P>$/0=$@M(N$/8$H1CC$/8$6B?27=$21F17E128$;:2$^1?$2/=7:4$@M(N4$/0=$P>$

H7CC$2/781$961$K<1897:0$/?:<9$6:H$96181$8B891L8$21C/91$9:$:01$/0:9612$70$912L8$:;$

K</C79BO$

$$

M09100/$8B891L8$/9$961$92/08L79912$8791$/0=$/C8:$21F17E12$/09100/8$011=$9:$?1$

1E/C</91=$;<29612O$R0$#0791=$%703=:L$/0=$R9/CB4$01H$0/97:0/C$89/0=/2=8$;:2$

F1297;7F/97:0$:;$@M($/0=$@M(N$21F17E128$/21$70$961$J2:F188$:;$?1703$J<?C7861=O$

"6181$01H$L196:=8$:;$L1/8<2703$IP$J12;:2L/0F1$/0=$;<0F97:0/C79B$:;$=7379/C$

2/=7:$21F17E1284$6:H1E124$=:$0:9$9/Y1$709:$/FF:<09$961$J12F17E1=$/<=7:$K</C79B$

F279127/$/8$J2181091=$70$9678$J/J12O$

$$

"61$F<22109$89<=B$78$L/=1$H796$89/97F$21F1J97:0O$M$C:03$=789/0F1$9:$961$

92/08L79912$L/B$=132/=1$961$/<=7:$70$E/27:<8$H/B8O$P:2$P>4$9678$218<C98$70$

L<C97J/96$=789:297:04$0:7814$19FO`$;:2$@M(4$=2:J:<98$/0=$:9612$/291;/F98$L/B$?1$

1E7=109O$>:21$1;;7F7109$122:2$F:221F97:0$L196:=8$/0=$H/B8$9:$70F21/81$;71C=$

892103964$1O3O4$?B$L1/08$:;$92/08L79912$J:H124$L/B$?1$011=1=4$18J1F7/CCB$;:2$

L:?7C1$21F1J97:0O$

$$

G730/C$C:88$L1/8<21L1098$<0=12$21/C7897F$21F1J97:0$F:0=797:08$H:<C=$37E1$/0$

/C9120/97E1$70912189703$1E/C</97:0$:;$961$:E12/CC$J12;:2L/0F1$:;$/$=7379/C$2/=7:$

!"#$

%%!&'($ $ )*+),*-,+.$

$ U.$

8B891LO$^6/9$H:<C=$?1$961$C78910128\$21/F97:08$9:$96181$9BJ18$:;$/291;/F98x$

$$

>:21$91898$F:<C=$FC/27;B$961$7LJ/F9$:;$C:H$?79$2/91$8B891L8$;:2$F:0927?<97:0$/0=$

J2:=<F97:0$70$F/8F/=1$H796$C:H$?79$2/918$<81=$;:2$=78927?<97:0$:;$=7379/C$2/=7:O$

"189$)$86:H1=$96/9$8:L1$:;$961$1;;1F98$:;$?/0=$C7L791=$/0F6:2$8730/C8$H121$

8<JJ21881=$H610$<8703$/$H7=1?/0=$/0F6:2$/8$=1;701=$70$G1F97:0$TO)OUO$"678$78$/$

J2:L78703$:?812E/97:0$/0=$F:LJ/278:0$:;$92/=797:0/CCB$<81=$/0F6:2$8730/C8$H796$

01H$9BJ18$:;$/0F6:2$8730/C8$86:<C=$?1$70E18973/91=$;<29612O$

$$

R0$8<LL/2B4$961$=1E1C:JL109$/0=$21;701L109$:;$8B891L8$;:2$/<=7:$?2:/=F/89703$

F/CC8$;:2$96:2:<36$9189703$/0=$F:LJ/278:0$:;$961$8B891L8$70$21C/97:0$9:$:9612$

/<=7:$8B891L8$/E/7C/?C1O$R0$8<F6$918984$/8$961$;70=7038$70$9678$J/J12$86:H4$79$H7CC$

?1$7LJ:29/09$9:$=1;701$F279127/$;:2$/FF1J9/?C1$K</C79B$/0=&:2$92/08J/210FBO$

W10F14$?:96$9189$=18730$/0=$F279127/$;:2$K</C79B$=1F787:08$/21$1881097/C$

F:LJ:01098$:;$;<9<21$2181/2F6O$

R J&#)0(3*&#3++":$F:0FC<=14$961$8B891L8$<0=12$9189$F:<C=$0:9$?1$F:087=121=$/8$?1703$;<CCB$

92/08J/2109$0:2$F:<C=$961B$:<9J12;:2L$/$21/C7897F$:J97L/C$P>$8B891L$:0$/CC$

/FF:<098O$"61$7LJC7F/97:08$:;$961$;70=7038$/21$C7891=$?1C:Hd$$

• "61$8<?F6/001C$?79$2/91$;:2$@M(N$86:<C=$0:9$?1$C188$96/0$+-)$Y?79&8$;:2$/$

89121:$8730/CO$$$

• M$@M(N$8<?F6/001C$?79$2/91$:;$+-)$Y?79&8$H:<C=$?1$F:LJ/2/?C1$9:$:2$

?19912$96/0$961$L:=120$P>$8B891LO$

• M$@M(N$8<?F6/001C$?79$2/91$:;$+S*$Y?79&8$H:<C=$?1$F:LJ/2/?C1$9:$:2$

?19912$96/0$961$/E12/31$9BJ18$:;$P>$92/08L799128$<81=$?B$GH1=786$I/=7:O$$

• (79$2/918$?1C:H$96181$F:<C=$87307;7F/09CB$=132/=1$961$K</C79B$:;$F129/70$

J2:32/LL1$L/9127/CO$

• ":$/FF:LL:=/91$;:2$L:21$F2797F/C$?<9$897CC$9BJ7F/C$791L84$<0C188$10F:=703$

7LJ2:E184$/$?79$2/91$FC:81$9:$T**$Y?79&8$L/B$?1$01F188/2B$;:2$J12F1J9</C$

92/08J/210FB$9:$?1$21/C781=O$

• ^610$L/Y703$=1F787:08$/?:<9$?2:/=F/89703$8B891L84$79$H7CC$?1$7LJ:29/09$

9:$6/E1$H1CC,=1;701=$F279127/$;:2$/FF1J9/?C1$K</C79B$/0=$H619612$

J12F1J9</C$92/08J/210FB$86:<C=$?1$21K<721=O$

$

5C1/2CB4$961$?79$2/918$/0=$10F:=128$70$9678$89<=B$H:<C=$6/E1$J2:?C1L8$Y11J703$<J$

H796$961$K</C79B$:;$:9612$6736,J12;:2L/0F1$/<=7:$/JJC7F/97:08$/E/7C/?C1$9:$961$

10=$<812O$"61$/E/7C/?C1$?79$2/918$/21$8<?[1F9$9:$21=<F97:0$=<1$9:$?:96$961$0<L?12$

:;$F:LJ19703$F6/001C8$96/9$86:<C=$?1$;7991=$/8$H1CC$/8$961$/0F7CC/2B$=/9/$70FC<=1=$

70$961$?79$8921/LO$R;$?2:/=F/89703$812E7F18$/=E:F/91$/0=$L/2Y19$/<=7:$K</C79B$/8$

96172$6/CCL/2Y4$709121891=$J/29718$011=$9:$L/Y1$H1CC,;:<0=1=$=1F787:08$:0$961$

/<=7:$F:=703$70;2/892<F9<21$H796$218J1F9$9:$F:0927?<97:04$=78927?<97:04$/0=$

1L7887:0O$

Z -)^#&I05'4565#$3+"61$/<96:28$H786$9:$96/0Y$961$J/297F7J/9703$8<?[1F98$70$961$C78910703$9189$;:2$96172$

97L1$/0=$1;;:29O$>2O$f126/2=$GJ7Y:;8Y7$VRI"X$/0=$>2O$>/967/8$5:70F6:0$VZ(#X$/21$

!"#$

%%!&'($ $ )*+),*-,+.$

$ US$

96/0Y1=$;:2$J2:E7=703$=/9/$/0=$8<JJ:29$;2:L$961$Z(#$@&@M(M$(cA$*-U$9189O$"61$

/<96:28$/21$32/91;<C$9:$>2O$!17;$5C/188:0$V5C/188:0,Z=H/2=84$]L07/$M<=7:$/0=$

!701/2$MF:<897FX$/0=$"12/F:L$;:2$J2:E7=703$P>$1K<7JL109O$P70/CCB4$@2O$P2/0F78$

I<L81B$/0=$>2O$M0=21H$>/8:0$/21$/FY0:HC1=31=$;:2$96172$E/C</?C1$F:LL1098O$

[ S5X5%5#)53++$

(10[/L7074$oO4$y$W:F6?1234$oO$V+--.XO$5:092:CC703$961$;/C81$=78F:E12B$2/91d$/$J2/F97F/C$/0=$

J:H12;<C$/JJ2:/F6$9:$L<C97JC1$9189703O$B,*#+-(),9)?,.-()1%-%&4%&$-()1,$&"%.8)1"#&"4)@)CD"%<,3,(,/&$-(EF)GH$V+X4$)i-,T**O$

Z(#O$V+---XO$I,$*2"+%)@JK)L7MN)?"!,#%),+)%<")4*'>"$%&O")(&4%"+&+/)%"4%4),9)2*(&%$<-++"()-*3&,)$,3"$48$f101E/d$Z<2:J1/0$(2:/=F/89703$#07:0O$

Z(#O$V)**-XO$I,$*2"+%)@JK)LM=N)1*'>"$%&O")544"442"+%)-+3)P'>"$%&O")2"-4*#"2"+%4),9)I5@Q8$f101E/d$Z<2:J1/0$(2:/=F/89703$#07:0O$

Z(#O$V)*+*XO$0@R)?"$,22"+3-%&,+)?)7:SN)T,*3+"44)+,#2-(&4-%&,+)-+3)!"#2&%%"3)2-U&2*2)("O"(),9)-*3&,)4&/+-(48$f101E/d$Z<2:J1/0$(2:/=F/89703$#07:0O$

Z(#O$V)**.XO$0@R)V"$<);;LMN)0O-(*-%&,+4),9)W-4$-3"3)5*3&,)W,3"$48$f101E/d$R09120/97:0/C$"1C1F:LL<07F/97:0$#07:0O$

Z(#O$V)**lXO$0@R)V"$<);;:=N)0@R)0O-(*-%&,+4),9)D*(%&$<-++"()5*3&,)W,3"$48$f101E/d$Z<2:J1/0$(2:/=F/89703$#07:0O$

Z(#O$V)*+*XO$0@R)V"$<);;;MN)0@R)0O-(*-%&,+4),9)D*(%&$<-++"()5*3&,)W,3"$4F)!<-4");8$f101E/d$Z<2:J1/0$(2:/=F/89703$#07:0O$

Z(#O$V)*++XO$0@R)V"$<);;==N)J#-$%&$-()/*&3"(&+"4)9,#)3&4%#&'*%&,+)4.4%"24)&+)-$$,#3-+$")X&%<)0@R)?)7:S8$f101E/d$Z<2:J1/0$(2:/F/89703$#07:0O$

Z"GRO$V)*+*XO$I&/&%-()5*3&,)@#,-3$-4%&+/)CI5@EY)V#-+4!,#%),9)53O-+$"3)5*3&,)W,3&+/)C55WE)-*3&,8$G:J67/,M097J:C78$51=1Qd$Z<2:J1/0$"1C1F:LL<07F/97:08$G9/0=/2=8$R08979<91O$

f173124$IO4$o<4$IO4$W12214$'O4$I/6/2=[/4$GO4$%7L4$GO4$!704$pO4$19$/CO$V)**lXO$RG]&RZ5$>cZf,U$

W736,@1;70797:0$GF/C/?C1$M=E/0F1=$M<=7:$5:=703O$B8)5*3&,)0+/8)1,$8$F)GG$V+&)X4$)l,UTO$

f2:884$%O$V)*++XO$"61$P<9<21$:;$W736,c12;:2L/0F1$>1=7/$A19H:2Y703O$501)==%<)6+%"#+-%&,+-()W,+9"#"+$"N)5*3&,)K"%X,#Z&+/8$A1H$o:2Yd$M<=7:$Z0370112703$G:F719BO$

R"#O$V+--lXO$?"$,22"+3-%&,+)6VR[?)@18777\[7N)D"%<,34)9,#)%<")4*'>"$%&O")-44"442"+%),9)42-(()&2!-&#2"+%4)&+)-*3&,)4.4%"24)&+$(*3&+/)2*(%&$<-++"()4,*+3)4.4%"248$f101E/d$R09120/97:0/C$"1C1F:LL<07F/97:0$#07:0O$

R"#O$V)**TXO$?"$,22"+3-%&,+)6VR[?)@187G;=[7N)D"%<,3)9,#)%<")4*'>"$%&O")-44"442"+%),9)&+%"#2"3&-%")]*-(&%.)("O"(),9)$,3&+/)4.4%"248$f101E/d$R0120/97:0/C$"1C1F:LL<07F/97:0$#07:0O$

R"#O$V+--iXO$?"$,22"+3-%&,+)6VR[?)@18=7:[MN)J(-++&+/)4%-+3-#34)9,#)%"##"4%#&-()^D)4,*+3)'#,-3$-4%&+/)-%)_`^8$f101E/d$R09120/97:0/C$"1C1F:LL<07F/97:0$#07:0O$

R"#O$V+--*XO$?"$,22"+3-%&,+)6VR[?)@18HLSN)I"%"#2&+-%&,+),9)%<")"("$%#,[-$,*4%&$-()!#,!"#%&"4),9)4%*3&,)2,+&%,#)<"-3!<,+"48$f101E/d$R09120/97:0/C$"1C1F:LL<07F/97:0$#07:0O$

%E/C14$GO4$y$(270YL/004$GO$V)**-XO$6+%"#_&"X4N)T"-#+&+/)%<")W#-9%),9)a*-(&%-%&O")?"4"-#$<)6+%"#O&"X&+/$V)0=$1=797:0$1=OXO$"6:<8/0=$]/Y8d$GMfZO$

>1227/L4$GO$(O$V)**-XO$a*-(&%-%&O")?"4"-#$<N)5)A*&3")%,)I"4&/+)-+3)62!("2"+%-%&,+8$G/0$P2/0F78F:d$^7C1BO$

A71C8104$GO4$y$!<0=64$"O$V)**TXO$]E12C:/=$70$8730/C$F:0E1287:0O$501):;#3)W,+9"#"+$"N)1&/+-()J#,$"44&+/)&+)5*3&,)?"$,#3&+/)-+3)?"!#,3*$%&,+8$A1H$o:2Yd$M<=7:$Z0370112703$G:F719BO$

!"#$

%%!&'($ $ )*+),*-,+.$

$ Ul$

G:<C:=214$fO$MO4$y$!/E:714$>O$5O$V+---XO$G<?[1F97E1$1E/C</97:0$:;$C/231$/0=$8L/CC$

7LJ/72L1098$70$/<=7:$F:=1F8O$501)7H%<)W,+9"#"+$"N)`&/<[a*-(&%.)5*3&,)W,3&+/8$A1H$o:2Yd$M<=7:$Z0370112703$G:F719BO$

"18F64$IO$V+--*XO$a*-(&%-%&O")?"4"-#$<N)-+-(.4&4)%.!"4)-+3)4,9%X-#")%,,(48$(/870389:Y1d$P/CL12$c218O$

$

+$

Paper 3: A Descriptive Method for Unravelling the Composition of the Black Box of Affective Judgment in Sound Quality Evaluation Submitted to: Journal of the Audio Engineering Society

Nyberg, Berg A Descriptive Method for…!

! "!

A Descriptive Method for Unravelling the Composition of the Black Box of Affective Judgment in Sound Quality Evaluation Dan Nyberg1, Jan Berg1

1Luleå University of Technology Abstract This paper presents a descriptive (qualitative) method for unravelling a detailed composition of affective judgment of audio quality. Interviews are used to collect data and condensation was used as the analysis method. This descriptive method was used in a listening test and the results from it are the basis of the discussion about the method applicability. The descriptive method is shown to collect detailed information about the subjects´ affective judgment including the subjects´ moods, expectations, attitudes, listening habits, and strategies when preference rankings as well as the associations the subjects have to perceived differences.

1. Introduction Typically, perceptual audio quality is analysed collecting data from three domains: the physical, the sensory, and the affective [2]. Several studies have been made on each domain separately as well as on the relation between the different domains. Affective measurements/judgments of audio quality, the focus of this study, includes the subjective preference of listeners. When affective judgments are conducted, the subjects (listeners) are assumed to combine context, expectations, previous experience, traditions, and any other attribute (e.g., spatial impression) in order to form an impression of the audio quality. The subject grades this judgment/measurement on a hedonic scale [1,2]. The data are then statistically analysed. Because any other deeper understanding of what goes into this judgment – e.g., how the subject feels, perceives, and understands the stimuli – are limited to the affective attribute under investigation, affective judgment can be seen as a “black box”. Since it is assumed that there are several aspects that go into affective judgment, identifying what composes affective judgment needs further investigation. This shifts the focus of research from a representative population to the individual subject’s experience. Understanding what affective judgment is composed of can provide information on how naïve listeners judge sound quality. Bech and Zacharov describe this relationship as follows:

The subject (listeners) is the central measuring device that supplies the basic data to be analysed, and the more the knowledge about the “instrument”, the easier it is to understand the data it provides. ([2], P.106)


! "!

This study uses a purely descriptive1 (qualitative) method (phenomenological analysis of interviews) as a first step to unravel “the black box” of affective judgment. A listening test is employed where semi-structured interviews are conducted before and during the test as naïve subjects make affective judgments in the form of preference rankings for perceptually encoded and PCM coded audio. Interviews are chosen as the data collecting method since they let the subjects verbally describe their perception freely. The interviews also help subjects reflect on how their affective judgment works. Rather than focusing on the results of the preference ranking, this study will focus on the following three issues:

• A qualitative analysis of the data; • The type of information collected with this methodology; and • The composition of the “black box”.

This study begins by providing background on material on affective judgment in perceptual audio research. Next, the study presents the framework of the descriptive research method. Then, the study presents the experimental setup, including an analysis of the methodology and results. Finally, the study discusses the methodology, the outcome, and the implications of the work.

2. Affective judgments This section presents information about how affective judgments are conducted, focusing on the choice of subjects and what biases exist in affective judgment.

2.1 Listeners When conducting affective measurement tests, un-trained/naïve subjects (listeners) are often used [2]. One reason for this is because they take less weight on spatial properties than trained subjects [2]. Also an indication and a discussion on this topic are taken in [3]. There is a downside using un-trained/naïve listeners, as they are shown to be less discriminating and less reliable compared with trained listeners when grading. However, regarding preference ratings, there are indications that the performance of un-trained listeners interested in sound quality and trained listeners is generally the same [4].

2.2 Bias in affective judgment In their extensive literary review, Zielinski et al. describe eight biases related to affective judgments [5]. The following section presents an overview of these biases. The first two biases – appearance and branding/labelling – relate to how subjects are influenced by the appearance, brand, or the label of the equipment being tested. The third bias, expectations, refers to a subject’s expectations regarding an object under investigation !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!$%&!'&()*+,-+.&!/&-%0'!+(!12(+)2334!2!5623+-2-+.&!/&-%0'7!16-!-%&!-&*/!'&()*+,-+.&!/&-%0'!8+.&(!2!1&--&*!+9'+)2-+09!0:!;%2-!'2-2!-%&!/&-%0'!2)-62334!)033&)-(7!'&()*+,-+09!0:!,&*)&,-+09(7!29'!;+33!1&!6(&'!'6*+98!-%&!)06*(&!0:!-%+(!(-6'4<!


! "!

and how well this expectation is fulfilled by the object [5]. This difference can give rise to bimodal or multimodal distribution of scores, indicating that the common practice of averaging and summarizing scores (mean values and confidence intervals) can lead to erroneous conclusions. The fourth bias presented is the non-acoustical factors of a sound, the meaning of the sound, in this paper refereed to as association bias. This bias is defined by what the subject associates with the sound. For example, Zielinski et al. [5], taking an example from Fastl [6], note that German subjects and Japanese subjects have different associations for the sound of a church bell: the Germans subjects interpreted the sound of the bells as “pleasant” and “safe” and Japanese subjects interpreted it as “dangerous” and “unpleasant”. The fifth bias, the personal preference bias, essentially is the change in preference by the subjects – changes to listening habits, changes in fashion, or changes in technical quality of products [5]. The sixth and seventh bias, emotions and mood, is caused by the subject’s reaction towards a specific stimulus. If the subject is happy, the scores tend to be more positive. The mood is the bias caused by the subject’s general attitude. If the subjects have a more positive frame of mind, they tend to grade the pleasantness of a sound significantly higher than subjects with a negative attitude [5]. The eighth bias, the situational context, is the result where the product is used. Thus the sound quality might be graded differently depending on the situational context. However, it is unclear whether this bias has an effect on the listener’s evaluation of a sound [5].

2.3 Implications of affective bias How to reduce the effects of these biases is debatable. One non-practical solution to reducing bias is to avoid tests involving affective judgments; however, this is not a good solution since affective judgment can provide useful information about market behaviour. A combination of sensory measurement and affective measurement can be applied. Thus this approach can provide additional descriptive information on products under investigation. In addition, it might be possible to increase the validity of affective judgment tests on several groups of listeners [5].

2.4 Bias involved in this research Since an affective measurement is assumed to be composed of several factors (context, mood, emotion, background, and expectation), it is evident that they all have an effect on judgment. Subjects can be asked questions that address the following biases:

• Appearance and branding/labelling biases • Expectation bias • Association bias • Personal preference bias • Emotion and mood bias • Context bias

This study does not address appearance and branding/labelling biases, as the subjects do not know what is under investigation. They are only instructed to rank the stimuli according to preference. In addition, the situational context bias cannot be investigated since the listening test is preformed in a controlled situation and in only one context. Only the subjects’ association to different situational contexts can be investigated.


! "!

3. Framework of the descriptive research method

3.1 Foundation As stated in the introduction, this work shifts the focus from generalization of many subjects’ perceptions to the individual subjective experience. This approach requires individuals to reflect on their experience and affective judgment. Thus this approach does not apply generalization and prediction of the results and conclusions on a selected population are therefore not available. Previous research [7, 8,9] on perception within this framework collects data from interviews and analyses the data using a grounded theory approach, generating a theory after the data is collected. That is, the researcher does not have a predetermined theory before collecting or analysing the data rather the data suggests the theory. This approach is also frequently used in exploratory research where little is known [10]. In this research, to some extent, it is known what an affective judgment is composed of (see section 2), but not to what extent and how it is integrated in the subject’s judgment, a characteristic that makes this research project more investigative than exploratory. This limitation makes the use of grounded theory less appropriate, since there is a specific topic under investigation – the composition of affective judgment. This study relies on a phenomenological approach where experiences and how the subject perceives experiences are in focus. The essences of an experience are the core meaning/meanings that are mutually understood through a phenomenon that is commonly experienced [11]. This description of phenomenology might seem diffuse, but it essentially means that when such an approach is put into the perceptual audio evaluation context, the phenomenon under investigation is what constitutes affective judgment and the essences address how individual subjects integrate cognitive factors into affective judgment (context, expectations, previous experience, traditions, spatial impression, etc.).

3.2 Listeners Qualitative research often uses purposeful sampling. This sampling technique is based on the assumption that the investigator wants to discover, understand, and gain insight into a phenomenon under investigation. Thus the sample selected, the subject, must have information and experience about the phenomenon [11].

3.3 Interviews In phenomenological approaches, data typically is collected during interviews. Roulston presents a neo-positivist’s conception of interviewing, which follows phenomenology and grounded theory assumptions [12]. This conception fits with this work since it focuses on the following research questions:

• What are the participant’s beliefs, perspectives, and attitudes according to X2? • What are the participant’s experiences in relation to X?

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!$%&!'!(&)&(*!+,!-.!&/0&(1&.2&3!45+!2,567!-6*,!4&!-.8!0%&.,9&.,.!5.7&(!*+578:!


! "!

In addition, Roulston [12] identifies methodological issues a researcher should consider when relying on this particular conception:

• The interviewer takes a neutral role in the interview, does not express his/her own perspective on the research topic.

• The interviewer minimizes research influence (i.e., bias) via the use of “open” and “non-leading” questions.

• The interviewer asks “good” questions in a sequence that generates “valid” and reliable data about the topic. ([12], p 205-206)

3.4 The foundation of the phenomenological analysis. To arrive at the essences of an experience, three steps are commonly used: bracketing, phenomenological reduction, and horizontalization. Bracketing (also called epoche) means that before starting the interviews the researcher should be aware of his/her prejudices, viewpoints, and assumptions of the phenomenon under investigation [11]. Phenomenological reduction is the process of reading and describing, looking and describing, etc. to comprehend the essences in the text and to isolate the phenomenon – understanding the text [12,13]. Phenomenological reduction and horizontalization are closely related. In horizontalization, the researcher examines and organises all the collected data (interview texts) into clusters or themes to make the essences clear. The results of this type of study are often a combined description that presents the essences of the investigated phenomenon, i.e., the structure of the experience [11]. Both phenomenological reduction and horizontalization can be done with different methods. The methods used in this research are meaning condensation, tables and graphical representation of the data, similar to mind maps. Meaning condensation, in short, is a method that condenses uttered meanings, transcribed interviews, into more essential meanings [14], condensing long explanations/descriptions into shorter manageable sentences. Meaning condensation follows the phenomenological reduction process. The tables and graphical representation were used to structure and categorize the data. See section 4.4 for a description of how the methods were used.

4. Experimental setup

4.1 Choice of Listeners The subject selection (n=3) used the guidelines of purposeful sampling as presented in 3.2. The criteria for the subjects were as follows: naïve listeners (no previous experience with listening tests) and frequent listeners of radio or other types of media involving audio. The choice of using naïve listeners is based on the information in section 2.1. Consequently, naïve listeners (i.e. without experience from formal listening tests) with some interest or knowledge of sound and sound quality were used, as they are more likely to have the same preference as trained listeners. The use of naïve listeners would also be more likely to collect a broader motivation not directly related to sound quality parameters or characteristics. This may allow the collection of other motivators of preference other then sound quality motivators, e.g. associations to the sound or the meaning of the sound to the listener.


! "!

4.2 Stimuli selection The stimuli used were audio files from MUSHRA [15] used in a joint experiment conducted by Swedish Radio and LTU. The stimuli were composed of speech (a female voice reading the news) and music (a pop/rock song). Each stimulus occurred in three versions; Two were perceptually coded at two different bit rates (48 kbit/s and 192 kbit/s, both using DAB+ coding) and one version was linear PCM (not perceptually coded). A total of six stimuli were used in the test. For more detailed information about how the stimuli were processed, see [15].

4.3 Listening test setup and interviews The experiment was conducted in an acoustically controlled environment. The equipment used was a studio grade soundcard (MOTU Ultralite Mk3), near field loudspeakers (Genelec 1030A), and one laptop computer running Windows 7 and the computer program STEP3. The loudspeaker was positioned in the regular 60° position. The listening levels were adjusted according to BS.1534-1 recommendations [16]. The order of the stimuli, both content and codecs, were randomized for each subject. The randomization process was done in the STEP software session file. Before the listening test, a short interview was conducted with each subject. The interview included questions about listening habits, expectations about the test, mood, and awareness of sound in different media. Then the subjects were instructed to rank the three versions for each stimulus, from best to worst. After each ranking (speech and music), an interview was conducted where the subject was asked to describe his/her ranking – which version was worse, best, etc. – and why they perceived them as best, worse, or in the middle. Additional questions were also asked about what method/strategy they used when conducting their ranking and if they associated the differences to something in particular. The interviewer was present during the entire test. Figure 1 provides a schematic of the test procedure. An interview guide provided the questions used during the interviews (Appendix 1).

4.4 Interview analysis The first step in the analysis was to use meaning condensation. The sentences created in the meaning condensation process were put into tables with different topics (Table 1). The second step of the analysis was to gain insight into how the processes of making an affective judgment are composed for each subject. The interviews were read again along with the results presented in the tables. From this analysis, a graphical representation was created that describes how each subject carried out their affective judgments. As a final step in the analysis, one single graphical representation of all the subjects’ affective judgments was created in order to show an outline of what the affective judgment was composed of. See Figure 2 for an outline of the analysis.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!$%%&'(()))*+,-./0121+03$4+52*3/6!


! "!

5. Results/Analysis This section, presenting the results of the interview analysis, describes the composition of affective judgment. Interesting findings in the data are also presented.

5.1 Composition of affective judgment Affective judgment is composed of several parts: the methods used by the subjects, the differences perceived, and how the subjects’ accomplished the ranking (Figure 3).

5.1.1 Subjects’ differentiating strategies From the analysis depicted in Figure 3 (Method), it can be seen that the subjects have several strategies for perceiving a difference in the stimuli. To discriminate differences, the strategies include first impressions, the whole, extremes, beyond the music, and specific parts. Beyond the music has a specific meaning:

I am also trying to listen beyond the music and the vocal; there is something in between which makes everything fit together… and this becomes often rough.

This indicates that this subject perceived a subtle quality degradation that is not related to the music but the overall quality. To discriminate differences, other subjects listened to specific parts and the proportion between the background sound and the verbal information in the stimuli (speech). One subject listened to the extremes to check for differences; when the subject became used to the extremes, the subject listened again to discriminate the differences even further.

5.1.2 Perceived differences From the analysis, the perceived differences could also be divided into two categories, see Figure 3. These differences are based on associating the different processed stimuli with different media:

A (PCM): a radio commercial (earache/very clear sound/the information comes thru); B (48 kpbs): student radio when a student that cannot handle the equipment right; and C (192 kbps): a kitchen radio with bad reception/windy.

The association of the differences included radio or television sound and how the particular stimuli (news or pop music) usually sound. One subject noted that at the beginning of the stimuli (speech) the sample sounded as if dynamic compression was applied to the sound and related the sound with the knowledge from the subject’s education. One subject associated each version of the music stimuli as being placed at different locations in the mix – next to the bass drum, next to the guitar player, and next to the lead singer. The direct differences included changes in frequency content between the stimuli, sharp consonants in speech, changes in level between instruments, breathing sound, and how well the listener comprehended the verbal information in the stimulus.

5.1.3 Ranking Figure 3 (Ranking) also divides the ranking process into two categories: 1) a ranking based on a combination of the associated differences and direct differences and 2) a


! "!

ranking based on association of the differences. In the former case, it seems to be a preference ranking, by two subjects, that incorporates both difference associations and direct differences. In the latter case, one subject created two separate rankings based on associated contexts, music stimuli. One ranking based on the subject’s own preference, if the subject listened at home, and another ranking based on if the subject was doing a radio show, transmitting radio. This subject made a ranking based on the target audience for the radio. When looking at the subject’s background, information elicited from the initial interview, it can be seen that the association relates to the subject’s background since the subject works with radio and Radio Theatre, indicating that the association is intertwined with the subject’s listening habits. The subject who ranked the stimuli two ways also ranked the speech stimuli in two ways: how quickly the quality was accepted (acceptance) and when the subject was used to the stimuli (quality). Although these rankings are not shown in Figure 2, they are evident in step 2 of the analysis. This type of ranking might indicate that there are several different ways to preference rank stimuli, making it hard for the subject to determine how to do the ranking.

5.2 Other findings

5.2.1 Mood/Expectations From the initial interview, one clear result is that the subjects’ mood and expectation on the test was positive. All three subjects used adjectives such as curious, exciting, little nervous, interested, tired but happy, focused on doing it right, and expectant when describing the mood and expectation of the test. The adjective little nervous was interpreted as positive since it was used in the dialog along with adjectives such as curious and exciting.

5.2.2 Comparison of listening habits, education/profession, and ranking When comparing each subjects’ answers on listening equipment and education/profession, extracted from the initial interview, with the subjects’ ranking (Table 2), one pattern emerges. Subject 2 listens to music mainly on the computer using Spotify free4 (software), and this subject also ranked the 48 kbps stimulus as most preferred because it was perceived as not as sharp as the other stimuli and had no frequency cut in the lower frequencies. This preference might indicate that the subject’s preference is low bitrate audio although further investigations are needed. Interestingly, the subjects who studied radio were more consistent than the subject who studied TV production. This difference might be due to the experience of listening to audio that the subjects studying radio have compared to studying TV production.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!#!$%&'(!&)*+,-./!*).!&+./-0'12!0%&'(!)**!+3/!41+/.1/+5!4+!'067/0/1+&!+3/!*).0-+!822!9).:'&!-+!:'+.-+/&!;<!=!>?@!A:6&BC!;D!=!>E@F!A:6&B!-1G!;?!=!><HF!A:6&B5!I3/!7-++/.!'&!-!6-J!&%:&(.'6+')1!KELM5!!


! "!

6. Discussion As stated, there are methodological issues regarding the interview: the interviewer does not express his/her own perspective on the research topic. To minimize the influence of the interviewer’s experience and expectations, open and non-leading questions were asked in this experiment. During the development of the interview guide, these issues were taken into consideration by creating open questions regarding the research topic and during the interview the interviewer focused on summarizing/interpreting the statements of the subject after long descriptive statements by the subjects. This strategy enabled the subjects to comment on the summary/interpretation directly during the interview, eliminating to some extent misinterpretation. During the design of the interview guide and implementation of the interviews, the researcher strived to become aware of his prejudices, viewpoints, and assumptions. Thus the interview questions focused on the subjects’ descriptions and viewpoints and not on what the researcher thought the subjects would perceive. In addition, the subject was encouraged to comment on the researcher’s summary and interpretations. These strategies helped establish an objective standpoint so as not to affect the results. This entire process can be categorized into the bracketing process. To make the structure of experience clear, the data was read and sorted – a process called horizontalization. Horizontalization – condensation, sorting, and creation of the graphical representations of the results – was performed during the analysis. From these, the structure of the affective measurement could be seen. This process depends on the researcher’s ability to scrutinize the data and involves a high amount of interpretation in order to make phenomenon clear. To further enhance the analysis, a second researcher’s analysis could be used as a comparison, checking for similarities and differences, improving the accuracy of the interpretation. When relating the results collected in the study to the biases in affective judgment posed by Zielinski et al. [5], there is clear evidence that the subject does incorporate them in the judgment. Association could be seen when distinguishing differences – associations related to radio commercial/student radio/radio with bad transmission reception, associating to different placements in the mix, and association to dynamic compression differences. The association could also be seen in the ranking process association to situational contexts, listening at home, or playing the stimuli on the radio. These associations could be a result imposed by the subjects trying to figure out what the differences between the stimuli are because this information was not presented to them during the experiment. The emotion and mood bias could be checked in the initial interview since it showed the subjects’ expectations, mood, and attitudes before the experiment, a strategy that could be helpful in any experiment. There were also indications of how the personal preference bias seems to affect affective judgment when comparing the subjects´ listening habits and education with their ranking: one subject who often listened to audio with low bitrates preferred 48 kbps. This conclusion is not certain since the subject’s rating could have been influenced by his/her inexperience with the speakers used in the experiment.


! "#!

The data collected contains information about the subjects’ listening habits, their initial attitudes towards the tests, what particular differences they perceived, how they evaluated the stimuli, how they associated the differences to different contexts, and what strategy they used to make an affective judgment. This information, of course, is highly dependent on the questions stated in the interviews, which depend on the research question and purpose posed. The important thing to notice from this work is that interviews and an interview analysis approach based on a phenomenological foundation can give insight into what is perceived and how the particular perceptions/judgments are composed. One could pose the following question. Why is this method useful and why should one use it when evaluating perceptual audio? By looking at the subjects’ descriptions of their perceptions, we can collect more information on what affects the results. By implementing interview and interview analysis of this sort for a numerical experiment, a better understanding of the perception can be achieved. Since researchers have no direct access to what is going on in other human minds, the easiest way to extract information is to systematically ask the subjects to describe their experience. There is also a possibility that a combination of “regular” affective judgment tests and this descriptive approach can show what biases are or are not affecting the results. Thus the decision-making process becomes easier for the researcher on further tests. The results from this study shed light on what an affective measurement is composed of. It also indicates that interviews and interview analysis can be valid methods for understanding what perceptions are composed of.

8. Conclusions This work shows that the descriptive method, interviews, and phenomenological analysis, where the subjects describe their perceptions freely, can shed light on what an affective judgment is composed of. In addition, the results show the methods used by the subject in order to discriminate the perceived differences, what the perceived differences are, and how the subjects incorporate background information, perceived differences, and association of the differences into their preference ranking. The results give an indication of a casual relationship between listening habits and preference ranking, but further research on this relationship is needed. The descriptive method can be fruitful when an understanding of how a perception is composed is needed.

Acknowledgements The author would like to thank Jonas Ekeroot and Dr. Anders Persson for their valuable comments on the manuscript.


! ""!

References 1. Lorho, G. Perceived quality evaluation: an application to sound reproduction over headphones. PhD thesis, Aalto University, Espoo. 2010, ISBN: 978-952-60-3195-8 2. Bech, Soren; Zacharov, Nick. Perceptual Audio Evaluation- Theory, Method and Application, John Wiley & Sons Ltd. Chichester, England, 2006, ISBN-13: 978-0-470-86923-9. 3. Lorho, G. The Effect of Loudspeaker Frequency Bandwidth Limitation and Stereo Base Width on Perceived Quality. Presented at the 120th AES Convention, Paris, France, May 20-23, 2006. 4. Olive, Sean E. Differences in Performance and Preference of Trained versus Untrained Listeners in Loudspeaker Tests: A Case Study. Journal of Audio Engineering Society, Vol. 51, No. 9, September, 2003 5. Zielinski, S; Rumsey, F; Bech, S. On Some Biases Encountered in Modern Audio Quality Listening Tests – A Review. Journal of the Audio Engineering Society, Vol 56, No. 6, June, 2008. 6. Fastl, H. Psycho-Acoustics and Sound Quality. Communication Acoustics, Blauert. J. Ed. Springer, Berlin, Germany, 2005. 7. Schulte-Fortkamp, B; Fiebig, A; Soundscape Analysis in a Residential Area: An Evaluation of Noise and People’s Mind. Acta Acoustica United with Acustica. Vol. 92, p 875-880, 2006. 8. Jumisko-Pyykkö, S; Reiter, U; Weigel, Chr, Produced Quality is Not Perceived Quality - A Qualitative Approach to Overall Audiovisual Quality; 3DTV Conference, May 7-9, 2007 9. Jumisko-Pyykkö, Satu; Häkkinen, Jukka; Nyman, Göte. Experienced Quality Factors-Qualitative Evaluation Approach to Audiovisual Quality. Proceedings of SPIE - The International Society of Optical Engineering, 6507, art. No, 65070M, 2007 10. Strauss, Anselm L.; Corbin, Juliet. Basics of Qualitative Research- Techniques and Procedures for Developing Grounded Theory, Second Edition. SAGE Publications, 2455 Teller Road, Thousands Oaks California 91320, 1998, ISBN 0-8039-5939-7 11. Marriam, Sharan. B. Qualitative Research, A Guide to Design and Implementation. Jossey--‐Bass, San Francisco, CA, US, 2009, ISBN: 978-0-470-28354-7 12. Roulston, Kathryn. Considering quality in qualitative interviewing. Qualitative Research, Vol. 10(2) pp. 199--‐228. 2010


! "#!

13. Moustakas, Clark E. Phenomenological research methods. SAGE publications, Inc. Thousand Oaks, California 91320, 1994, ISBN 0-8039-5799-8. 14. Kvale, Steinar; Brinkmann, Svend. InterViews - Learning the Craft of Qualitative Research Interviewing, Second Edition. SAGE Publications, Inc. 2455 Teller Road, Thousand Oaks, California 91320, 2009, ISBN 978-0-7619-2541-5. 15. Percived audio quality of realistic FM and DAB+ radio broadcasting systems. Berg, J; Bustad, C; Jonsson, L; Mossberg, L; Nyberg, D. Submitted to the Journal of Audio Engineering Socieity 16. BS.1534-1; Methods for the subjective assessment of intermediate quality level of coding system, ITU-R Recommendations (2001-2003) 17. Information about Codec quality, FAQ Spotify. URL: http://www.spotify.com/se/help/faq/tech/codec-quality/ Collected 2012-05-14


! "#!

Figure captions Figure 1: Listening test procedure. X1 and X2 are one of the two stimuli: speech or music. Figure 2: Steps in the analysis Figure 3: Composition of affective judgment Table 1: Topics used for categorizing sentences Table 2: Comparison between listening habits, education/profession, and ranking. PCM (Non-perceptually coded material), 192 kbps (perceptually coded material with an bit rate of 192 kbps) and 48 kbps (perceptually coded material with an bit rate of 48 kbps). Appendix 1: Interview guide translated from Swedish.


! "#!

Figure 1:

Figure 2:

$%&'&()!&%'*+,&*-!

.&/'*%&%0!1%!

/'&23)&!4"!

$%'*+,&*-!1%!

/'&23)&!4"!

.&/'*%&%0!1%!

/'&23)&!45!

$%'*+,&*-!1%!

/'&23)&!45!


! "#!

Figure 3: Table 1: Topics Categories Listening habits

Time spent listening to music/audio in general

Formats • Portable media player • Mobile phone • Computer • Home stereo system • Software used • Headphones • Loudspeakers

Content • Musical preference • What the subjects listen

to.

Expectations/moods/attitudes before the test and on the test.

Perceived differences

Music Speech

Ranking Music Speech

Association Music Speech

Method used for looking at differences

Music Speech


! "#!

Table 2: Subject Listening equipment

(listening habits) Education/Profession

Ranking Speech Music

1 • Mobile phone • Headphones

Studying TV production

1. PCM (C) 2. 48 kbps (B) 3. 192 kbps (A)

1. 192 kbps (A) 2. PCM (B) 3. 48 kbps (C)

2 • Mainly Computer and Spotify free (software)

• Computer loudspeakers • Computer connected to

external loudspeakers (radio)

• Sometimes using a portable media player (iPod)

Studying radio 1. 48 kbps (B) 2. 192 kbps (A) 3. PCM (C)

1. 48 kbps (C) 2. 192 kbps (B)/PCM 3. (A) (hard to determine ranking order)

3 • Mobile phone • Radio • Computer • Computer loudspeaker • Computer connected to

Hi-fi loudspeakers • iTunes (AAC files) • Sometimes Spotify

Studying radio. Works with; radio theatre and audio.

Acceptance Used to the stimuli

Listening at home

Played on the radio

1. 192 kbps (C) 2. PCM (A) 3. 48 kbps (B)

1. PCM 2. 192 kbps 3. 48 kbps

1. 192 kbps (B) 2. PCM (C) 3. 48 kbps (A)

1. PCM 2. 192 kbps 3. 48 kbps

Appendix 1: Initial interview What is your favourite music? Give examples of music genre you prefer Give examples of music genre you do not prefer How much do you spend listening to music or sounds (radio, TV, or other)? Where do you listen? Radio? Mobile phone/portable media player? Internet/Spotify/iTunes? CD/Vinyl? Other media? What are you expectations on the test? Are you interested? Is it going to be fun conducting the test? Do you want to “get it over with”? Do you feel curious? (translated from Swedish word nyfiken) Do you feel insecure? Do you feel uncomfortable? What is going to happen during the test? What is your mood?


! "#!

Nervous? Expectant? Happy? Sad? Angry? Sulky? (translated from Swedish word sur) Questions during the listening phase What sound is the best according to your opinion? Why? What sound is the worst according to your opinion? Why? What sound is in between these two? Why? In comparison with both the best and worse, which one is closer? Do you perceive any differences in the sound?

Describe the differences Between which sounds do you perceive a difference?

Do you listen to the overall sound or are there any specific parts in which you listen to? Which parts? What is it that you listen to in particular? Do you associate the sound to something when you compare the stimuli? If yes, can you describe this “something”? In which part of the sound do you perceive the differences? When do the differences become apparent? What are the characteristics of the differences? How do you go on when comparing the sounds/stimuli?

Documents

An Investigation of Qualitative Research …990443/...focus varies between describing a sensation objectively and evaluating the audio subjectively, depending on the research focus