Sound Events and Emotions: Investigating theRelation of Rhythmic Characteristics and Arousal

K. Drossos 1 R. Kotsakis 2 G. Kalliris 2 A. Floros 1

1Digital Audio Processing & Applications Group, Audiovisual Signal ProcessingLaboratory, Dept. of Audiovisual Arts, Ionian University, Corfu, Greece

2Laboratory of Electronic Media, Dept. of Journalism and Mass Communication, AristotleUniversity of Thessaloniki, Thessaloniki, Greece

Agenda

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Structured form of soundGeneral sound

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Everyday life

Everyday life

StimuliMany stimuli, e.g.

VisualAural

Structured form of soundGeneral sound

Interaction with stimuliReactions

Emotions felt

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions

MusicStructured form of sound

Primary used to mimic and extent voice characteristics

Enhancing emotion(s) conveyanceRelation to emotions through (but not olny) MIR and MER

Music Information RetrievalMusic Emotion Recognition

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

Music and emotions (cont’d)

Research results & Applications

Accuracy results up to 85%In large music data bases

Opposed to typical “Artist/Genre/Year” classificationCategorisation according to emotionRetrieval based on emotion

Preliminary applications for synthesis of music based on emotion

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events

Sound eventsNon structured/general form of soundAdditional information regarding:

Source attributesEnvironment attributesSound producing mechanism attributes

Apparent almost any time, if not always, in everyday life

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound and emotions

From music to sound events (cont’d)

Sound eventsUsed in many applications:

Audio interactions/interfacesVideo gamesSoundscapesArtificial acoustic environments

Trigger reactions

Elicit emotions

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Objectives of current study

DataAural stimuli can elicit emotions

Music is a structured form of soundSound events are not

Music’s rhythm is arousing

Research questionIs sound events’ rhythm arousing too?

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Objectives of current study

DataAural stimuli can elicit emotions

Music is a structured form of soundSound events are not

Music’s rhythm is arousing

Research questionIs sound events’ rhythm arousing too?

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Objectives of current study

DataAural stimuli can elicit emotions

Music is a structured form of soundSound events are not

Music’s rhythm is arousing

Research questionIs sound events’ rhythm arousing too?

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Objectives of current study

DataAural stimuli can elicit emotions

Music is a structured form of soundSound events are not

Music’s rhythm is arousing

Research questionIs sound events’ rhythm arousing too?

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Experimental Procedure’s Layout

Layout

Emotional model selection

Annotated sound corpus collectionProcessing of sounds

Pre-processingFeature extraction

Machine learning tests

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Sound corpus & emotional model

General Characteristics

Sound CorpusInternational Affective Digital Sounds (IADS)

167 sounds

Duration: 6 secs

Variable sampling frequency

Emotional modelContinuous model

Sounds annotated using Arousal-Valence-Dominance

Self Assessment Manikin (S.A.M.) used

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Pre-processing

General procedureNormalisation

Segmentation / WindowingClustering in two classes

Class A: 24 membersClass B: 143 members

Segmentation/WindowingDifferent window lengths

Ranging from 0.8 to 2.0 seconds, with 0.2 seconds increment

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Feature Extraction

Extracted featuresOnly rhythm related features

For each segment

Statistical measures

Total of 26 features’ set

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Feature Extraction

Extracted featuresOnly rhythm related features

For each segment

Statistical measures

Total of 26 features’ set

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Feature Extraction

Extracted featuresOnly rhythm related features

For each segment

Statistical measures

Total of 26 features’ set

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Feature Extraction

Extracted featuresOnly rhythm related features

For each segment

Statistical measures

Total of 26 features’ set

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Processing of Sounds

Feature Extraction

Extracted featuresOnly rhythmrelated features

For eachsegment

Statisticalmeasures

Extracted Features Statistical MeasuresBeat spectrum MeanOnsets Standard deviationTempo GradientFluctuation KurtosisEvent density SkewnessPulse clarity

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Feature Evaluation

ObjectivesMost valuable features for arousal detection

Dependencies between selected features and different windowlengths

Algorithms usedInfoGainAttributeEval

SVMAttributeEval

WEKA software

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Feature Evaluation

ObjectivesMost valuable features for arousal detection

Dependencies between selected features and different windowlengths

Algorithms usedInfoGainAttributeEval

SVMAttributeEval

WEKA software

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Feature Evaluation

ObjectivesMost valuable features for arousal detection

Dependencies between selected features and different windowlengths

Algorithms usedInfoGainAttributeEval

SVMAttributeEval

WEKA software

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Classification

ObjectivesArousal classification based on rhythm

Dependencies between different window lengths andclassification results

Algorithms usedArtificial neural networks

Logistic regression

K Nearest Neighbors

WEKA software

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Classification

ObjectivesArousal classification based on rhythm

Dependencies between different window lengths andclassification results

Algorithms usedArtificial neural networks

Logistic regression

K Nearest Neighbors

WEKA software

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Machine learning tests

Classification

ObjectivesArousal classification based on rhythm

Dependencies between different window lengths andclassification results

Algorithms usedArtificial neural networks

Logistic regression

K Nearest Neighbors

WEKA software

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

Feature Evaluation

General resultsTwo groups of features formed, 13 features each group

An upper and a lower group

Inner group rank not always the same

Upper and lower groups constant for all window lengths

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

Feature Evaluation

General resultsTwo groups of features formed, 13 features each group

An upper and a lower group

Inner group rank not always the same

Upper and lower groups constant for all window lengths

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

Feature Evaluation

General resultsTwo groups of features formed, 13 features each group

An upper and a lower group

Inner group rank not always the same

Upper and lower groups constant for all window lengths

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

Feature Evaluation

General resultsTwo groups of features formed, 13 features each group

An upper and a lower group

Inner group rank not always the same

Upper and lower groups constant for all window lengths

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

Feature Evaluation

Upper group

Beatspectrum std

Event density std

Onsets gradient

Fluctuation kurtosis

Beatspectrum gradient

Pulse clarity std

Fluctuation mean

Fluctuation std

Fluctuation skewness

Onsets skewness

Pulse clarity kurtosis

Event density kurtosis

Onsets mean

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%

Lowest accuracy score: 71.26%

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%

Lowest accuracy score: 71.26%

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%

Lowest accuracy score: 71.26%

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%Window length: 1.0 secondAlgorithm utilised: Logistic regression

Lowest accuracy score: 71.26%

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%Window length: 1.0 secondAlgorithm utilised: Logistic regression

Lowest accuracy score: 71.26%

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification

Classification

General resultsRelative un-corelation of features

Variations regarding different window lengths

Accuracy results

Highest accuracy score: 88.37%Window length: 1.0 secondAlgorithm utilised: Logistic regression

Lowest accuracy score: 71.26%Window length: 1.4 secondsAlgorithm utilised: Artificial Neural network

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature evaluation

FeaturesMost informative features:

Rhythm’s periodicity at auditory channel (fluctuation)OnsetsEvent densityBeatspecturmPulse clarity

Independent from semantic content

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification results

Algorithms related

LR’s minimum score was: 81.44%

KNN’s minimum score was: 82.05%

Results relatedSound events’ rhythm affects arousal

Thus, sound’s rhythm affect arousal

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification results

Algorithms related

LR’s minimum score was: 81.44%

KNN’s minimum score was: 82.05%

Results relatedSound events’ rhythm affects arousal

Thus, sound’s rhythm affect arousal

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification results

Algorithms related

LR’s minimum score was: 81.44%

KNN’s minimum score was: 82.05%

Results relatedSound events’ rhythm affects arousal

Thus, sound’s rhythm affect arousal

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Classification results

Algorithms related

LR’s minimum score was: 81.44%

KNN’s minimum score was: 82.05%

Results relatedSound events’ rhythm affects arousal

Thus, sound’s rhythm affect arousal

1. IntroductionEveryday lifeSound and emotions

2. Objectives

3. Experimental SequenceExperimental Procedure’s LayoutSound corpus & emotional modelProcessing of SoundsMachine learning tests

4. ResultsFeature evaluationClassification

5. Discussion & Conclusions

6. Feature work

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature work

Features & DimensionsOther features related to arousal

Connection of features with valence

Introduction Objectives Experimental Sequence Results Discussion & Conclusions Feature work

Feature work

Features & DimensionsOther features related to arousal

Connection of features with valence

Thank you!