Department of Electrical Engineering - DiVA portal23741/FULLTEXT01.pdf · Department of Electrical Engineering Examensarbete ... the driver’s cabin of a Valmet 890 forest machine

Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Active Noise Control of a Forest Machine Cabin

Examensarbete utfört i Reglerteknikvid Tekniska högskolan i Linköping

av

Patrik GrylinMårten Hedborg

LITH-ISY-EX--07/4014--SE

Linköping 2007

Department of Electrical Engineering Linköpings tekniska högskolaLinköpings universitet Linköpings universitetSE-581 83 Linköping, Sweden 581 83 Linköping

Active Noise Control of a Forest Machine Cabin

Examensarbete utfört i Automatic Controlvid Tekniska högskolan i Linköping

av

Patrik GrylinMårten Hedborg

LITH-ISY-EX--07/4014--SE

Handledare: Johan Sjöbergisy, Linköpings universitet

Kjell RönnholmKomatsu Forest AB

Examinator: Fredrik Gustafssonisy, Linköpings universitet

Linköping, 22 March, 2007

Avdelning, InstitutionDivision, Department

Division of Automatic ControlDepartment of Electrical EngineeringLinköpings universitetSE-581 83 Linköping, Sweden

DatumDate

2007-03-22

SpråkLanguage

� Svenska/Swedish� Engelska/English

�

�

RapporttypReport category

� Licentiatavhandling� Examensarbete� C-uppsats� D-uppsats� Övrig rapport�

�

URL för elektronisk versionhttp://www.control.isy.liu.sehttp://www.ep.liu.se/2007/4014

ISBN—

ISRNLITH-ISY-EX--07/4014--SE

Serietitel och serienummerTitle of series, numbering

ISSN—

TitelTitle

Aktiv Bullerdämpning av Förarhytt på SkogsmaskinActive Noise Control of a Forest Machine Cabin

FörfattareAuthor

Patrik Grylin Mårten Hedborg

SammanfattningAbstract

Today, a high noise level is considered a problem in many working environments.The main reason is that it contributes to stress and fatigue. Traditional methodsusing passive noise control is only practicable for high frequencies. As a comple-ment to passive noise control, active noise control (ANC) can be used to reducelow frequency noise. The main idea of ANC is to use destructive interference ofwaves to cancel disturbing noises.

The purpose of this thesis is to design and implement an ANC system inthe driver’s cabin of a Valmet 890 forest machine. The engine boom is one of themost disturbing noises and therefore the main subjective for the ANC system tosuppress.

The ANC system is implemented on a Texas Instrument DSP developmentstarter kit. Different FxLMS algorithms are evaluated with feedback and feedfor-ward configurations.

The results indicate that an ANC system significantly reduces the soundpressure level (SPL) in the cabin. Best performance of the evaluated systemsis achieved for the feedforward FxLMS system. For a commonly used enginespeed of 1500 rpm, the SPL is reduced with 17 dB. The results show fast enoughconvergence and global suppression of low frequency noise.

NyckelordKeywords Active Noise Cancellation, Adaptive Filtering, Active Noise Control, Feedforward,

Feedback, Active Noise Reduction

AbstractToday, a high noise level is considered a problem in many working environments.The main reason is that it contributes to stress and fatigue. Traditional methodsusing passive noise control is only practicable for high frequencies. As a comple-ment to passive noise control, active noise control (ANC) can be used to reducelow frequency noise. The main idea of ANC is to use destructive interference ofwaves to cancel disturbing noises.

The purpose of this thesis is to design and implement an ANC system in thedriver’s cabin of a Valmet 890 forest machine. The engine boom is one of themost disturbing noises and therefore the main subjective for the ANC system tosuppress.

The ANC system is implemented on a Texas Instrument DSP development starterkit. Different FxLMS algorithms are evaluated with feedback and feedforwardconfigurations.

The results indicate that an ANC system significantly reduces the sound pressurelevel (SPL) in the cabin. Best performance of the evaluated systems is achievedfor the feedforward FxLMS system. For a commonly used engine speed of 1500rpm, the SPL is reduced with 17 dB. The results show fast enough convergenceand global suppression of low frequency noise.

v

Acknowledgments

First of all, we would like to express our gratitude to Fredrik Gustafsson and Jo-han Sjöberg at Linköping university. Of course, for their help during our master’sthesis but most of all for being excellent and inspiring teachers.

Kjell Rönnholm, Per Holmberg, Per Hedström and others - building the world’sbest forest machines - for help and support during our time at Komatsu Forest.

Mårten Nygren for kickstarting our project with valuable comments and sugges-tions.

Josse, grandpa Lasse, Fredrik & Co, Oscar G. & Kent P. at the division of Elec-tronic Systems, Y3c, Kuratorvägen and Uno-x in Bjurholm deserves to be men-tioned as well.

Patrik Grylin and Mårten Hedborg

vii

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 The concept of ANC . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.6.1 Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6.2 Transfer functions . . . . . . . . . . . . . . . . . . . . . . . 41.6.3 Impulse responses, filter parameters . . . . . . . . . . . . . 41.6.4 Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.6.5 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 ANC principles 72.1 Acoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Wave equation . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.2 Principle of superposition . . . . . . . . . . . . . . . . . . . 7

2.2 Feedforward system overview . . . . . . . . . . . . . . . . . . . . . 82.2.1 Primary path . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 Secondary path . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.3 Digital filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Feedback system overview . . . . . . . . . . . . . . . . . . . . . . . 102.3.1 Secondary path . . . . . . . . . . . . . . . . . . . . . . . . . 102.3.2 Digital filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 ANC adaptive filter theory 133.1 Finite Impulse Response . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Least Mean Square . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Normalized LMS . . . . . . . . . . . . . . . . . . . . . . . . 143.2.2 Leaky LMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3 Feedforward filtered-x LMS . . . . . . . . . . . . . . . . . . . . . . 163.3.1 Feedforward Multi Channel FxLMS . . . . . . . . . . . . . 17

3.4 Feedback FxLMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.4.1 Feedback MC-FxLMS . . . . . . . . . . . . . . . . . . . . . 20

ix

3.5 Secondary path identification . . . . . . . . . . . . . . . . . . . . . 213.5.1 Choosing an input signal u(n) . . . . . . . . . . . . . . . . . 213.5.2 System latency . . . . . . . . . . . . . . . . . . . . . . . . . 22

4 Implementation 234.1 DSP hardware and equipment . . . . . . . . . . . . . . . . . . . . . 23

4.1.1 Development Starter Kit . . . . . . . . . . . . . . . . . . . . 244.1.2 DSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.2 DSP software concepts and techniques . . . . . . . . . . . . . . . . 254.2.1 Code composer studio . . . . . . . . . . . . . . . . . . . . . 254.2.2 DSP/BIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2.3 PING-PONG buffering . . . . . . . . . . . . . . . . . . . . . 25

4.3 DSP program design . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.1 Main function . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.2 DSP/BIOS threads . . . . . . . . . . . . . . . . . . . . . . . 274.3.3 ANC system states . . . . . . . . . . . . . . . . . . . . . . . 27

4.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . 27

5 Results 295.1 Feedforward FxLMS . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.1.1 Primary path . . . . . . . . . . . . . . . . . . . . . . . . . . 295.1.2 Secondary path . . . . . . . . . . . . . . . . . . . . . . . . . 315.1.3 Constant engine speeds . . . . . . . . . . . . . . . . . . . . 315.1.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2 Feedback FxLMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.1 Secondary path . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.2 Constant engine speeds . . . . . . . . . . . . . . . . . . . . 385.2.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.3 Global noise reduction in the cabin . . . . . . . . . . . . . . . . . . 435.4 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6 Concluding Remarks 476.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Bibliography 49

A Equipment 51

Chapter 1

Introduction

1.1 BackgroundToday, a high noise level is considered a problem in many working environments.The main reason is that it contributes to stress and fatigue [3]. Traditional meth-ods using passive noise control are only practicable for high frequencies. Thereason is that low frequency noise has long wavelengths compared to a typicalacoustic absorber [4]. As a complement to passive noise control, active noise con-trol (ANC) can be used to reduce low frequency noise. The main idea of ANC isto use destructive interference of waves to cancel disturbing noises. The theoryof ANC has been known for a long time but it is the development of fast digitalsignal processors (DSP:s) that have made practical applications feasible.

1.2 PurposeThe purpose of this thesis is to reduce the noise level in the driver’s cabin of aValmet 890 forest machine, see Figure 1.1, by using an ANC system. One of themost disturbing noises is the engine boom which is the main noise to be suppressedby the ANC system.

1.3 MethodThe project was divided into four main parts:

Literature studies: Old reports and articles concerning ANC were examined forideas and knowledge.

Design of a prototype: To be able to build a prototype, system hardware hadto be specified.

Implementation: ANC algorithms were implemented in a DSP.

Evaluation: The algorithms were evaluated online in the cabin.

1

2 Introduction

Figure 1.1. Valmet 890.

1.4 The concept of ANCThe concept of ANC is to cancel a disturbing primary noise by generating ananti noise of equal amplitude and opposite phase. The primary noise combinedwith the anti noise results in a cancellation of both noises. To generate the antinoise, two main configurations of ANC systems are often used, feedforward andfeedback control, both using adaptive filters [4, 9]. For a feedforward ANC systemtwo sensors are used. First a reference sensor is used to get a reference signaland second an error sensor is used to update the adaptive filter by measuring theresidual of the anti noise and primary noise, see Figure 1.2. The feedback ANCsystem uses only one sensor, namely an error sensor. This sensor is used both tocreate the reference signal and to update the adaptive filter, see Figure 1.3. Bothfeedforward and feedback configurations will be evaluated in this thesis.

1.5 Thesis outline 3

ANCAlgorithm

Error SensorReference SensorPrimary Noise Anti-Noise

Figure 1.2. An ANC feedforward system.

ANCAlgorithm

Error SensorPrimary Noise Anti-Noise

Figure 1.3. An ANC feedback system.

1.5 Thesis outline

Chapter 1 An introduction to the thesis where the background and purpose arepresented.

Chapter 2 Fundamental principles for active noise control are introduced.

Chapter 3 A presentation of ANC adaptive theory is given.

Chapter 4 The implementation of the system in a DSP is discussed.

Chapter 5 The results of the work are given.

Chapter 6 Conclusions and suggestions for future work are presented.

4 Introduction

1.6 Nomenclature

1.6.1 Signals

x(n) Reference signald(n) Primary noise at the error sensor positiony(n) Adaptive filter outputy′(n) Adaptive filter output after passing through a secondary pathe(n) Error signalx′(n) Reference signal after passing through a secondary path estimatione0(n) Gaussian random noisev(n) Generated Gaussian random noiseu(n) Low pass filtered input for system identificationy′′(n) u(n) after passing through a secondary path including unwanted noiseX(z) Z -transform of reference signalE(z) Z -transform of error signal

1.6.2 Transfer functions

P (z) Primary pathF (z) General filterS(z) Secondary pathS(z) Secondary path estimationS(z|s) Secondary path estimation during its identificationW (z|w) FIR filterH(z) Transfer function representing unwanted noise, driven by Gaussian

random noiseLP (z) Low-pass filter

1.6.3 Impulse responses, filter parameters

w, w(n) Filter parameters of W (z|w)s(n) Impulse response of S(z)p(n) Impulse response of P (z)s(n) Impulse response of S(z)s Filter parameters of S(z)s Filter parameters of S(z|s)

1.6 Nomenclature 5

1.6.4 Constantsµ Step sizeα Very small numberγ Leaky constantν 1 − µγN Number of parameters in w(n)J Number of reference sensorsM Number of error sensorsK Number of secondary sourcesnτ Time delay of secondary pathMτ Number of delays for calculating cross correlationNτ Number of samples when calculating cross correlation

1.6.5 AbbreviationsAD Analog DigitalANC Active Noise ControlBIOS Basic Input Output SystemCCStudio Code Composer StudioCPU Central Processing UnitDA Digital AnalogDRAM Dynamic Random Access MemoryDSK Development Starter KitDSP Digital Signal ProcessorEDMA Enhanced Direct Memory AccessFIR Finite Impulse ResponseFxLMS Filtered x Least Mean SquareLMS Least Mean SquareMcBSP Multi Channel Buffered Serial PortMC-FxLMS Multi Channel Filtered x Least Mean SquareMSE Mean Square ErrorNLMS Normalized Least Mean Squarerpm Revolutions Per MinuteSPL Sound Pressure LevelTI Texas InstrumentsUSB Universal Serial Bus

6 Introduction

Chapter 2

ANC principles

In this chapter fundamental ANC principles and phenomenas are introduced.

2.1 Acoustics2.1.1 Wave equationA three dimensional harmonic wave can be expressed as

ψ(r, t) = Aek·r−ωt (2.1)

with angle velocity ω, time t, and

k · r = kxx+ kyy + kzz (2.2)

where (kx, ky, kz) are the components of the propagation direction and (x, y, z) arethe components of the point in space where the displacement ψ is evaluated.

The differential wave equation satisfied by the harmonic wave (2.1) is given by

∇2ψ − 1c20

δ2ψ

δt2= 0 (2.3)

where the Laplacian operator is defined as

∇2 =δ2

δx2+

δ2

δy2+

δ2

δz2(2.4)

and c0 is the propagation speed in air. More information about wave equationscan for example be found in [12].

2.1.2 Principle of superpositionThe fundamental physical phenomena making an ANC system possible is theprinciple of superposition. The principle of superposition states that a linear

7

8 ANC principles

combination of solutions is also a solution to the same linear system. To be morelegible, if ψ1 and ψ2 are independent solutions of the wave equation (2.3), thenthe linear combination

ψ = aψ1 + bψ2 (2.5)

where a and b are constants, is also a solution [12].

Since the propagation of an acoustic wave is described fairly well by (2.3) alsoin practise, it is possible to use an anti noise of equal amplitude and oppositephase, see Figure 2.1.

Hence the objective for an ANC system is to measure the primary noise andgenerate the anti noise.

0 1 2 3 4 5 6 7 8 9 10−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Primary noiseSecondary noisePrimary noise + Secondary noise

Figure 2.1. Principle of superposition.

2.2 Feedforward system overviewIn Figure 2.2, an overview of an ANC feedforward system is illustrated. Thesummation junction in the block diagram represents an acoustic superposition ofsound waves.

2.2 Feedforward system overview 9

P (z)

F (z) S(z)

Σ−

x(n)

y(n)

d(n)

y′(n)

e(n)

Electrical domain Acoustical domain

Figure 2.2. Overview of an ANC feedforward system.

2.2.1 Primary pathThe primary path P (z) is the acoustic transfer function from the reference sensorclose to the noise source, to the error sensor at the point where the ANC system isoperating. The primary noise being measured in an upstream position is referredto as x(n) and the primary noise at the error sensors is referred to as d(n).

2.2.2 Secondary pathThe secondary path S(z) describes the transfer function from the filter outputy(n) to y′(n), the acoustic point where the error sensor is placed. This transferfunction includes computer system delays, amplifier and speaker dynamics and theacoustic path between speaker and error sensor. The speaker generating the antinoise is often referred to as the secondary source.

2.2.3 Digital filterThe noise x(n) is usually measured with a microphone and used by the digital filterF (z) as a reference signal. As an output, the digital filter produces the signal y(n)which is phase shifted and driven through the secondary source to become y′(n)at the point where to cancel out d(n). The acoustic signal y′(n) is often referredto as the anti noise or secondary noise. The error signal e(n) = d(n) − y′(n) atthis point is measured with a microphone and used by the digital filter to improvethe estimation y(n).

In general, the digital filter F (z) contains an estimation W (z|w) of the trans-fer function P (z)S−1(z) and an adaptive algorithm which derives the estimatedparameters w. The estimation W (z|w) is normally a finite impulse response (FIR)filter, that is a standard filter in acoustic path modeling [6], see Section 3.1.

10 ANC principles

To improve F (z), other subsystems such as an estimation of S(z) are often in-cluded and some are described in Section 3.

A fundamental system limitation

For a feedforward system, the time delay of P (z) has to be longer then the timedelay of F (z) and S(z). If this causality condition is met, the ANC system is capa-ble of cancelling random broad-band noise. Otherwise, only predictable periodicnoise can be cancelled out.

2.3 Feedback system overviewIn Figure 2.3, an overview of an ANC feedback system is illustrated. The differencefrom a feedforward system is that no reference sensor is used and thus no primarypath exists. Instead an internal synthesized reference signal is used to generatey(n), see Section 3.4.

F (z) S(z)

Σ−

d(n)

y(n)

y′(n)

e(n)

Electrical domain Acoustical domain

Figure 2.3. Overview of an ANC feedback system.

2.3.1 Secondary pathAs for the feedforward system, a secondary path exists for the feedback system.For more information, see Section 2.2.2

2.3.2 Digital filterThe digital filter works the same way as in a feedforward system, see Section 2.2.3.The main difference is that the reference signal is synthesized instead of measuredfrom a reference microphone.

2.3 Feedback system overview 11

A fundamental system limitation

Since no reference signal from the primary noise exists in a feedback system, goodperformance is only achieved for periodic noise.

12 ANC principles

Chapter 3

ANC adaptive filter theory

Here different adaptive filter algorithms for ANC applications are presented.

3.1 Finite Impulse ResponseThe most commonly used filter for ANC applications is the FIR filter. The as-sumption of a FIR filter is that the output y(n) depends on a weighted combinationof a finite number N of past input values x(n), expressed as

y(n) =N∑

i=0

wix(n− i) (3.1)

A FIR filter has a number of useful properties, e.g., all poles are at the originwhich means that it is bounded input bounded output (BIBO) stable [5].

3.2 Least Mean SquareIn this section the least mean square (LMS) algorithm for a system using a singlereference input, a single error sensor and a single secondary source is derived. AFIR filter W (z|w) of order N and the signal definitions according to Figure 3.1are used. Assume that the output of the filter is given as

y(n) = x(n)T w(n) (3.2)

wherex(n) =

(x(n− 1), ..., x(n−N)

)T (3.3)

andw(n) =

(w1(n), w2(n), ..., wN (n)

)T. (3.4)

Using (3.3) and (3.4), an expression for the error signal can be found as

e(n) = d(n) − y′(n) = d(n) − s(n) ∗ [x(n)T w(n)] (3.5)

13

14 ANC adaptive filter theory

P (z)

W (z|w)

LMS

S(z)

Σ−

x(n)

y(n)

d(n)

y′(n)

e(n)

Electrical domain Acoustic domain

Figure 3.1. Block diagram of a feedforward LMS system.

where s(n) is the impulse response of the transfer function S(z). The primary dis-turbance signal is given by d(n) = p(n) ∗ x(n), where p(n) is the impulse responseof P (z), and the secondary noise is given by y′(n) = s(n) ∗ y(n).

Assuming a mean square error (MSE) as a theoretical cost function

V (w) = E{e2(n)} (3.6)

the LMS-algorithm minimizes an estimation of (3.6), that is,

V (w) = e2(n). (3.7)

Using the steepest descent algorithm the LMS-algorithm is finally given by

w(n+ 1) = w(n) − µ

2dV (w)dw (3.8)

where µ is the step size, see Section 3.2.1. An example of the steepest descentalgorithm is illustrated in Figure 3.2.

3.2.1 Normalized LMSThe step size, µ in (3.8), is a design parameter that affects the convergence rateand the MSE. Large step sizes increase the convergence rate but will also result inan increased MSE, while small step sizes lead to small MSE but slow convergencerate. To decrease the influence of the amplitudes of x(n) and d(n), µ is normalized

3.2 Least Mean Square 15

−50

0

50

−50

0

50

0

1000

2000

3000

4000

5000

w1

w2

V(w

)

Figure 3.2. Convergence of V (w) towards the global optimum using LMS for a FIRfilter of order 2.

by the energy of x(n) i.e.

µ(n) =µ

α+ xT (n)x(n)(3.9)

often referred to as normalized LMS (NLMS). To prevent singularities whenxT (n)x(n) is very small, a small number α is introduced.

3.2.2 Leaky LMSANC systems are often overloading their secondary sources, making the systemnonlinear. Instead of introducing output power constraints, a modified cost func-tion

Vl(w) = e2(n) + γwT (n)w(n) (3.10)

including the weighted filter parameters can be used. This approach has severaladvantages. It has a stabilizing effect on the adaptive filter, and in finite-precisionimplementations it reduces numerical errors [9]. Using (3.8) and (3.10), the up-dating algorithm of the leaky LMS filter parameters become

w(n+ 1) = νw(n) − µ

2dV (w)dw

(3.11)

whereν = 1 − γµ. (3.12)

A disadvantage with introducing the leaky parameter, ν, is that it adds a biaserror to the residual.


3.3 Feedforward filtered-x LMSTo be able to use the LMS algorithm (3.8) in an ANC application the system hasto compensate for the effect of the secondary path transfer function. Otherwise,when updating the filter parameters the error signal is not aligned in time withthe reference signal and this will lead to instability [9]. An algorithm that com-pensates for this effect is derived below.

From (3.7) we obtain

dV (w)dw

=de2(n)dw

= 2[de(n)dw

]e(n) (3.13)

and the differentiation of (3.5) yields

de(n)dw

= −s(n) ∗ x(n). (3.14)

Combining (3.8), (3.13) and (3.14) yields

w(n+ 1) = w(n) − µ

2dV (w)dw

= w(n) − µ

22de(n)dw

e(n)

= w(n) + µ[s(n) ∗ x(n)]e(n). (3.15)

Here, the secondary path impulse response s(n) is not known and has to be esti-mated by s(n).

By substituting s(n) with s(n) and defining

x′(n) � s(n) ∗ x(n) =(x′(n), ..., x′(n−N + 1)

)T (3.16)

the expression (3.15) can finally be written as

w(n+ 1) = w(n) + µx′(n)e(n) (3.17)

also known as the feedforward filtered-x LMS (FxLMS) algorithm [11], illustratedin Figure 3.3. The z-transform of the error signal is

E(z) = [P (z) − S(z)W (z|w)]X(z) (3.18)

and to achieve a residual error equal to zero it is necessary that

W (z|w) =P (z)S(z)

, (3.19)

which is possible if the causality condition described in Section 2.2.3 is satisfied.

3.3 Feedforward filtered-x LMS 17

P (z)

W (z|w)

LMSS(z)

S(z)

Σ−

x(n)

y(n)

d(n)

y′(n)

x′(n)

e(n)


Figure 3.3. Block diagram of a feedforward FxLMS system.

3.3.1 Feedforward Multi Channel FxLMSA commonly used notation for multi channel systems is J ×M ×K, meaning thatJ reference inputs, M error sensors and K secondary sources are used. In general,the theory for the single channel system can be applied on the multi dimensionalcase, but some functions have to be redefined.

For a J ×M ×K system the FIR filter can be expressed as

yk(n) =J∑

j=1

xj(n)T w(n)kj k = 1, 2, ...,K (3.20)

and the cost function is approximated by

V (w) =M∑

m=1

e2m(n). (3.21)

The parameters of the FIR filters will be updated according to

wkj(n+1) = wkj(n)+µM∑

m=1

x′jkm(n)em(n) k = 1, 2, ...,K; j = 1, 2, ..., J (3.22)

where

x′jkm(n) � smk(n) ∗ xj(n) k = 1, 2, ...,K; m = 1, 2, ...,M. (3.23)


The feedforward multi channel FxLMS (MC-FxLMS) system is illustrated in Fig-ure 3.4. The block S represents M ×K secondary path transfer functions Smk(z).The matrix P represents M × J primary path transfer functions Pmj(z) and theK × J separate adaptive FIR filters are represented by W .

P

W

LMSS

S

Σ−

x(n)J

Jy(n)K

d(n)M

y′(n)M

Mx′(n)

J

e(n)

Figure 3.4. Block diagram of a feedforward MC-FxLMS system.

3.4 Feedback FxLMSA feedback FxLMS system is viewed as a feedforward FxLMS system that synte-sizes its own reference signal based on the adaptive filter output and error signal.The reference signal x(n) is synthesized as

x(n) = e(n) + s(n) ∗ y(n) (3.24)

where s(n) is the impulse response of S(z), e(n) is the signal obtained from theerror sensor and y(n) is the secondary signal generated from the adaptive filter.In Figure 3.5, a feedback FxLMS system is illustrated.

If S(z) = S(z) the z-transform of the error signal is

E(z) = [1 − S(z)W (z|w)]D(z) (3.25)

and the block diagram representing this equation is shown in Figure 3.6. In thisway, the feedback problem has been transformed into a feedforward problem withW (z|w) acting as a predictor for the primary noise d(n).

By viewing the feedback FxLMS system as in Figure 3.6 the importance of twostatements made earlier is emphasized; the importance of a quality estimate ofS(z) and the need for d(n) to be predictable, see Section 3.3 and 2.3.2, respec-tively.

3.4 Feedback FxLMS 19

W (z|w)

LMSS(z)

S(z)

S(z)

Σ−

Σ

d(n)

y(n)

y′(n)

x′(n)

x(n) e(n)


Figure 3.5. Block diagram of a feedback FxLMS system.

S(z) W (z)

LMS

Σ-

d(n)

e(n)

Figure 3.6. Equivalent block diagram to the system in Figure 3.5, under the assumptionthat S(z) = S(z).


3.4.1 Feedback MC-FxLMSExtending the earlier presented single channel FxLMS feedback methode to a mul-tiple channel feedback FxLMS system is fairly easy, see Figure (3.7).

A K ×M feedback MC-FxLMS system consists of K ×M secondary path trans-fer functions Smk(z) from the k:th secondary source to the m:th error sensor,estimated by the corresponding filter Smk(z). To synthesize M reference signals

W

LMSS

S

S

Σ−

Σ

d(n)M

y(n)K

y′(n)M

Mx′(n)

x(n)M

e(n)

Figure 3.7. Block diagram of a feedback MC-FxLMS system.

xm(n), M error signals em(n), M × K secondary path estimates Smk(z) and Ksecondary signals yk(n) are used according to

xm(n) = em(n) +K∑

k=0

smk(n) ∗ yk(n) (3.26)

where m = 1, 2, ...,M .

The coefficients of the K ×M adaptive filters Wkm(z) are updated by the MC-FxLMS algorithm similarly as in Section 3.3.1, for the special case J = M .

3.5 Secondary path identification 21

3.5 Secondary path identification

Finding an estimate of the secondary path transfer function S(z) is done by systemidentification. As seen below, adaptive filtering is closely related to the widersubject of system identification.

LP (z)White NoiseGenerator

H(z)

LMS

S(z)

S(z|s)

Σ

Σ−

v(n) u(n)

e0(n)

y′′(n)

e(n)


Figure 3.8. Block diagram of a system estimating S(z) with S(z|s).

There are several recursive identification algorithms to choose between. A blockdiagram of a recursive LMS algorithm estimating S(z) with a FIR filter S(z|s) isshown in Figure 3.8.

3.5.1 Choosing an input signal u(n)

To identify the secondary path low-pass filtered white noise is generated and driventhrough the secondary source. White noise is a random noise signal that has thesame sound energy level at all frequencies, i.e., its spectrum can be written as

Φ(ω) = C, ∀ ω ∈ [0,∞] (3.27)

where C is a constant. To assure that a proper model of the secondary path isderived for the lower frequencies, a low-pass filter is used to concentrate the energyto these frequencies [10]. The reason for this is explained below.

First, rewrite the MSE cost function estimate (3.7) using the inverse Fourier trans-


form

V (s) = e2(n) =12π

π∫−π

Φe(ω)dω (3.28)

where Φe(ω) is the spectrum of the prediction errors {e(n)}. The system is givenby

y′′(n) = S(z)u(n) +H(z)e0(n) (3.29)

where S(z) is the true system, u(n) are system inputs and unwanted noise isrepresented by H(z), a inversely stable monic1 filter driven by white noise {e0(n)}.Then, if u and e0 are independent, the following expressions are valid

s = arg minsV (θ) = arg min

s

π∫−π

∣∣∣S(eiω) − S(eiω|s)∣∣∣2Q(ω)dω (3.30)

Q(ω) =Φu(ω)

|H(eiω)|2 (3.31)

where s are the FIR filter parameters of S(z) identified by the process.For all frequencies, the difference between S(z) and S(z) is weighted with Q(w)which is dependent of the input energy for all frequencies. Hence the lower fre-quencies will be dominating in the minimization (3.30) when using a low-pass filter[10].

3.5.2 System latencyThe secondary path S(z) has a latency that needs to be included in the estimationand it is measured in amount of samples nτ . Cross correlation is a method forestimating the similarity between signals, in this case u(n) and y′′(n). A highcross correlation value corresponds to high similarity between signals.

By calculating the cross correlation between u(n) and y′′(n+ τ)

Ruy′′(τ) = limNτ→∞

1Nτ

Nτ∑n=1

u(n)y′′(n+ τ) (3.32)

for different delays τ = 0, 1, 2...Mτ . The time delay, nτ , is chosen as the τ whereRuy′′(τ) is significantly greater than zero.

1A filter whose direct term is equal to 1

Chapter 4

Implementation

In this chapter the implementation of the ANC system is presented.

4.1 DSP hardware and equipment

The computer platform for the ANC system is a Development Starter Kit TMS320C6713DSP from Texas Instruments. Other equipments such as amplifiers, microphonesand speakers are also used in different setups. More information about the equip-ment can be found in Appendix A.

Figure 4.1. TMS320C6713 DSP DSK.

23

24 Implementation

4.1.1 Development Starter KitThe Development Starter Kit (DSK) is a card built to test and evaluate theTMS320C6713 DSP, see Figure 4.1. Some key features of the DSK include:

• A Texas Instrument floating point digital signal processor TMS320C6713operating at 225MHz.

• An AIC23 audio codec (AD/DA converter) with stereo input and output.

• 16 Mbytes of synchronous DRAM.

• 512 Kbytes of non-volatile Flash memory.

• USB communication with a host computer used for software development.

4.1.2 DSPIn Figure 4.2, a block diagram of the TMS320C6713 DSP is shown. The processorhas many features, two of importance for the implementation of the ANC systemare described below.

EMIF

McBSPs

Timers

HPI

Interrupt

Selector

Power DownLogic

BootConfiguration

Enhanced

DMA

Controller

PLL

L2 Memory

L1P Cache

C6000 DSP Core

A Reg. File

Data Path A

L1 S1 M1 D1

B Reg. File

Data Path B

D2 M2 S2 L2

L1D Cache

Instruction Fetch

Instruction Dispatch

Instruction Decode

Interrupt

Control

In-CircuitEmulation

Test

ControlLogic

ControlRegisters

Figure 4.2. A block diagram of a TMS320C6713 DSP.

Multichannel Buffered Serial Port

The Multichannel Buffered Serial Port (McBSP) is a serial port that buffer samplesautomatically with the aid of the EDMA controller, described below. It consistsof a data path and a control path, which connect to external devices, and four

4.2 DSP software concepts and techniques 25

other pins that control clocking and frame synchronization. The DSP consists oftwo McBSPs that are used in the ANC system to communicate with the AD/DAconverter. The first one, McBSP0, is used for control and configuration and thesecond one, McBSP1, is used to transfer data between the AD/DA converter andthe CPU. [16]

Enhanced Direct Memory Access

Enhanced Direct Memory Access (EDMA) allows devices to transfer data inde-pendently of the CPU. Typically, block data transfers and transfer requests fromperipherals, such as the AIC23 audio codec, are performed by the EDMA thusrelieving the CPU to do performance-intensive operations. In this system, theEDMA controller is configured to take every 16-bit audio sample arriving on theMcBSP1 and store it in a buffer in the memory until it can be processed. Once thedata have been processed, the EDMA controller sends it back to the McBSP1. [15]

4.2 DSP software concepts and techniques4.2.1 Code composer studioCode composer studio (CCStudio) is a development tool used for programmingthe TMS320C6713 DSP. It is part of Texas Instrument’s real-time eXpressDSPTM

software and development tool strategy. The program is run on a host PC andcommunicates with the target DSP through a USB interface.

4.2.2 DSP/BIOSDSP/BIOS is a real-time kernel that is used on the TMS320C6713 [17]. It isdesigned for real-time applications and supports scheduling, synchronization, real-time instrumentation and host-to-target communication. It is designed to min-imize memory and CPU requirements on the DSP. There are many DSP/BIOSoptions for program development and some are listed below:

• Several thread types are supported such as hardware interrupts, softwareinterrupts, tasks, periodic tasks and idle functions.

• Semaphores, mailboxes and resource locks are provided to support commu-nication and synchronization between threads.

• It is possible to use both dynamically and statically created objects.

Writing programs using the DSP/BIOS kernel is done by using CCStudio.

4.2.3 PING-PONG bufferingPING-PONG buffering is a technique where two buffers (referred to as the PINGbuffer and the PONG buffer) are used for a data transfer instead of only one. Ifonly a single buffer is used, problems with new data overwriting the data being

26 Implementation

transmitted will occur. Using the PING-PONG technique, data in PING buffer isbeing processed while the next set of data is read into the PONG buffer, and viceversa. This ANC system is using PING-PONG buffers both when transmittingand receiving, for a total of four buffers.

4.3 DSP program designThe ANC program is implemented using the language C and an overview of thedesign solution is given in this section. In Figure 4.3 the design solution is illus-trated.

ANC-offSTATES

ANC-onIdentification

Mainfunction

DSP/BIOSIdle-loop edmaHwi

processBufferSwi

THREADS

return

Buffering

completed

EDMA Interrupt

Buffering not completedreturn

DSP/BIOSKernel

Figure 4.3. An overview of the ANC program design solution.

4.3.1 Main functionWhen first starting the program the main function is run. This is done only onceand outside the DSP/BIOS real-time kernel. It initiates all functions, hardwaresetups and libraries the ANC program needs when entering the DSP/BIOS kernel.Important properties of the main function include:

• Initializing of the audio AIC23 codec on the DSK. The codec sample fre-quency is set to 8000 Hz and returns an integer of 16 bits.

• Initializing of the McBSP interface towards the AIC23.

4.4 Summary and conclusion 27

• The EDMA controller is set to transfer samples from the McBSP to the CPUcore using PING-PONG buffering.

• Memory space used by arrays in the ANC algorithm is cleared.

After the program has returned from the main function it enters the DSP/BIOSkernel and the DSP/BIOS idle loop.

4.3.2 DSP/BIOS threadsThree threads are used

• The DSP/BIOS idle-loop is the thread with lowest priority. This thread isused for communication between the target (DSP) and the host (PC runningCCStudio).

• edmaHwi is a hardware interrupt and is the highest prioritized thread. It istrigged when a complete DMA frame has been received by the EDMA. If boththe receive and transmit transfers have been completed, the processBufferSwiis trigged.

• processBufferSwi is a software interrupt that is trigged by the edmaHwi.This task executes the signal processing and identification algorithms de-scribed in Chapter 3 depending on the present state of the the system.

4.3.3 ANC system statesThe ANC system has three different states, ANC-on, ANC-off and identifica-tion implemented in the processBufferSwi task. Switching between the differentstates is done by editing two variables using a feature in CCStudio called watchwindow.

• Variable ident_onoff is set to 1/0 for on/off. Initially 0.

• Variable anc_onoff is set to 1/0 for on/off. Initially 0.

In identification mode, the system identifies the secondary path according to Sec-tion 3.5, when completed ident_onoff is automatically set to 0.

4.4 Summary and conclusionImplementing the ANC system using the TMS320C6713 DSK has both advantagesand disadvantages. It is fairly easy to use and there are lots of open code writtenalready. On the other hand, without adding extra hardware the ANC system islimited to use only two microphones and two speakers. Adding hardware is costlyand extra code has to be written.

Comparing a DSP platform to a PC platform, the DSP is by far more faster and

28 Implementation

efficient. As an example, the loop through a system including sound card, speaker,air, microphone and back again takes 80 ms on a Matlab/Simulink implementa-tion using Windows XP. The same loop with the laptop replaced by a DSP takesonly 3.6 ms. Hence, there is a big difference and for real time implementation aDSP implementation is preferable.

Chapter 5

Results

In this chapter, detailed results for the feedforward and feedback system are pre-sented in Section 5.1 and Section 5.2, respectively. Due to the way the ANCsystems are implemented, the DSP is halted for an instant when turning on thesystems. This is causing a noise impulse, visible in some time plots. To calculatethe reduction of sound pressure level (SPL), following formula is being used

Lp = 20 log10

(poff

pon

)dB (5.1)

where Lp is the SPL reduction, poff is the 8000 samples root mean square errorbeing measured when the ANC system is off and pon when the ANC system is on.

5.1 Feedforward FxLMS

An overview of a feedforward system was presented in Section 2.2 and theory isavailable in Section 3.3. During the evaluation of the feedforward system twomicrophones are used, the reference microphone is put close to the engine and theerror microphone is put close to the ceiling of the cabin.

5.1.1 Primary path

To achieve a good result, a strong dependence between noise in the referencemicrophone and noise in the error microphone is necessary. The dependence ishere illustrated by coherence estimate plots. Coherence is a function of frequencywith values between 0 and 1 that indicate how well two signals corresponds toeach other at each frequency. A value of 1 indicates a strong dependence. InFigure 5.1, Figure 5.2 and Figure 5.3, coherence estimate plots are used to illustratethe dependence between error and reference singal for the engine speeds of 840,1500 and 2150 revolutions per minute (rpm).

29

30 Results

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Frequency (kHz)

Mag

nitu

de (

dB)

Coherence Estimate via Welch

Figure 5.1. Coherence estimate between reference michrophone and error microphoneat an engine speed of 840 rpm.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Frequency (kHz)

Mag

nitu

de (

dB)



5.1 Feedforward FxLMS 31

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Frequency (kHz)

Mag

nitu

de (

dB)



5.1.2 Secondary pathIn Section 3.5 the theory of secondary path identification was presented. Low-passfiltered Gaussian random noise is used to estimate the secondary path and by usingcross correlation the system delay is estimated. In Figure 5.4 the cross correlationbetween the output noise and the measured noise is shown at different time delays.The result of the secondary path identification is presented in Figure 5.5 wherethe parameters of the FIR filter S(z) are plotted. Since the delay of the systemwas 57 samples during the identification, the same amount of samples are set tozero in s, see Figure 5.5.

5.1.3 Constant engine speedsTo get a feeling of how well the ANC system is working for different engine speedsthree cases are studied: idling engine speed, maximum engine speed and an enginespeed of 1500 rpm. The engine speed 1500 rpm is a commonly used engine speedfor maximum performance and economical driving and will in the rest of thisreport be referred to as working enginge speed.

Idling engine speed (840 rpm)

In Figure 5.6, a distinct difference is visible for the operating ANC system andthe convergence of the system is seen during the time interval 2 − 2.3 seconds.

32 Results

0 100 200 300 400 500

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

Lag

Sam

ple

Cro

ss C

orre

latio

n

Sample Cross Correlation Function (XCF)

Figure 5.4. Cross correlation between time delays.

0 100 200 300 400 500−5

−4

−3

−2

−1

0

1

2

3

4

5x 10

−3

i

s i

Figure 5.5. Filter parameters si, i = 1,...,560, of S(z).


The SPL is reduced with 15.5 dB. A plot of the power spectral density for theANC system off and on is shown in Figure 5.7. Significant differences are visibleespecially for the peaks at 45 Hz and 90 Hz, but other frequencies are suppressedas well, while a few are enhanced.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)

Figure 5.6. Time plot of error signal. The feedforward ANC system is turned on attime 2 s. Engine speed 840 rpm.

Working engine speed (1500 rpm)

A significant difference is visible for the operating ANC system, see Figure 5.8.Looking at the power spectral density plot, Figure 5.9, the peak of 84 Hz is sup-pressed well. The SPL is reduced with 17.5 dB.

Maximum engine speed (2150 rpm)

For an engine running full speed, the ANC system is not making that big differenceas for the previous cases, see Figure 5.10. The low frequenices do not have asmuch energy as in the previous cases, but the peak of 55 Hz is suppressed well,see Figure 5.11. The SPL is reduced with 2.5 dB.

5.1.4 Robustness

Robustness is crucial for a usable system. To evaluate the robustness two simpletests are done: ANC turned on with open door and ANC running while closingthe door.

34 Results

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−120

−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)

Periodogram Power Spectral Density Estimate

ANC offANC on

Figure 5.7. Power spectral density of error signal for the feedforward ANC system.Engine speed 840 rpm.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)



0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−120

−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)


ANC offANC on


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)


36 Results

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−120

−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)


ANC offANC on


ANC turned on with open door

In the first case the secondary path is changed since identification of S(z) wasdone with closed door. When the ANC system is turned on it is easy to see howthe system converges despite a model error in S(z), see Figure 5.12.

ANC running while closing the door

In the second case the door is open and the ANC system is running with a modelerror in S(z). By closing the door, S(z) is changed back to normal. In Figure 5.13it is visible that despite the change of the secondary path and a violent noise fromthe closing of the door, the system is working fine.


0 1 2 3 4 5 6 7 8 9 10−0.03

−0.02

−0.01

0

0.01

0.02

0.03

Time (s)

Figure 5.12. Time plot of error signal with open door. The feedforward ANC systemis turned on at time 5.8 s. Engine speed 840 rpm.

0 1 2 3 4 5 6 7 8 9 10

−0.1

−0.05

0

0.05

0.1

0.15

Time (s)

Figure 5.13. Time plot of error signal. The feedforward ANC system is running andthe door is closed at time 7 s. Engine speed 840 rpm.

38 Results

5.2 Feedback FxLMSAn overview of a feedback system was presented in Section 2.3 and the theory isbriefly described in Section 3.4. During the evaluation of the feedback system onlyone error microphone is used and it is put close to the ceiling of the cabin.

5.2.1 Secondary path

The identification of the secondary path for the feedback system is identical as forthe feedforward system, see Section 5.1.2.

5.2.2 Constant engine speeds

As for the feedforward system, three different engine speeds are studied for thefeedback system: idling engine speed, maximum engine speed and working enginespeed.

Idling engine speed (840 rpm)

In Figure 5.14, a distinct difference is visible for the operating ANC system andthe convergence of the system is seen during the time interval 2 − 2.3 seconds.The SPL is reduced with 10.7 dB. A plot of the power spectral density is shownin Figure 5.15.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)

Figure 5.14. Time plot of error signal. The feedback ANC system is turned on at time2 s. Engine speed 840 rpm.


0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−120

−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)


ANC offANC on

Figure 5.15. Power spectral density of error signal for the feedback ANC system.Engine speed 840 rpm.

40 Results

Working engine speed (1500 rpm)

The time plot for the operating ANC system is shown in Figure 5.16 and the powerspectral density plot is presented in Figure 5.17. The SPL is reduced with 14.9 dB.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)


Maximum engine speed (2150 rpm)

For an engine running full speed, the ANC system is not making a big difference,see Figure 5.18. The peak of 118 Hz is suppressed, see Figure 5.19, and the SPLis reduced with 4.8 dB.

5.2.3 RobustnessAs for the feedforward system, two simple tests are done to evaluate the robustnessfor the feedback system: ANC turned on with open door and ANC running whileclosing the door.

ANC turned on with open door

The feedback system does not become unstable with a significant model error inS(z) caused by an open door, see Figure 5.20.

ANC running while closing the door

An impulse provocation of the system, closing the door, does not make the systemunstable, see Figure 5.21.


0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−120

−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)


ANC offANC on


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.01

−0.008

−0.006

−0.004

−0.002

0

0.002

0.004

0.006

0.008

0.01

Time (s)


42 Results

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2−110

−100

−90

−80

−70

−60

−50

−40

−30

Frequency (kHz)

Pow

er/fr

eque

ncy

(dB

/Hz)

(R

el. a

rbitr

ary

refe

renc

e)


ANC offANC on


0 1 2 3 4 5 6 7 8 9 10−0.025

−0.02

−0.015

−0.01

−0.005

0

0.005

0.01

0.015

0.02

0.025

Time (s)

Figure 5.20. Time plot of error signal with open door. Feedback ANC system turnedon at time 5.8 s. Engine speed 840 rpm.

5.3 Global noise reduction in the cabin 43

0 1 2 3 4 5 6 7 8 9 10−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

0.25

Time (s)

Figure 5.21. Time plot of error signal. The feedback ANC system is running and thedoor is closed at time 5 s. Engine speed 840 rpm.

5.3 Global noise reduction in the cabinAll results in this section are measured from the error microphone. But sincethe noise is minimized at this position, the noise level does not necessary have tobe decreased elsewhere in the cabin. To visualize what the driver is hearing, amonitor microphone is put close to the ear of the driver. In Figure 5.22 and 5.23,the error microphone and monitor microphone are presented for an idling engine.In this case, the feedback ANC system is used but the results are similar to thefeedforward ANC system. The plots show that suppression is achieved globally inthe cabin and not just locally at the position of the error microphone.

44 Results

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

Time (s)


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

Time (s)

Figure 5.23. Time plot of monitor signal. The feedback ANC system is turned on attime 2 s. Engine speed 840 rpm.

5.4 Summary of results 45

5.4 Summary of resultsA summary of the results is presented in Table 5.1.

∆ SPLrpm Feedforward Feedback840 15.5 dB 10.7 dB1500 17.5 dB 14.9 dB2150 2.5 dB 4.8 dB

Table 5.1. Summary of results

46 Results

Chapter 6

Concluding Remarks

In this chapter conclusions and suggestions of future work are discussed.

6.1 ConclusionsIn this thesis we have evaluated active noise control of a forest machine cabin.Two different sensor configurations have been studied, feedforward and feedback.The feedforward system uses a reference sensor to get a reference signal, while thefeedback system generates its own reference signal from the error signal and theadaptive filter output. In both cases the adaptive filter is updated by the errorsensor. Microphones are being used as sensors in the evaluated systems. TheFxLMS algorithm is being used for both feedforward and feedback to compensatefor different effects that comes up running the system in real time.

The results show that the systems works best for idling engine speed and for anormal working engine speed, mainly because of the low frequency noise generatedat these engine speeds. Both the feedforward and the feedback system show fastenough convergence and global suppression of low frequency noise in the cabin.

6.2 Future workAs a result of the time limit of the project, improvements of the system are pos-sible and some suggestions of future work follows below.

• For a feedforward system other reference sensors than microphones, such asan engine speed sensor, should be evaluated.

• Evaluation of reference sensors at other positions should be done to cancelout other disturbing noises, e.g., one possible position is close to the hydraulicpump.

47

48 Concluding Remarks

• A combination of a feedforward ANC and a feedback ANC system, oftenreferred to as a hybrid ANC should be evaluated since good results havebeen achieved in other projects [14], [8].

• Place speakers and microphones at the driver’s seat, close to the ears, toachieve better results at higher frequencies as a complement to the existingsubwoofer.

• To increase system performance, other step size algorithms should be eval-uated, e.g., different variable step size algorithms have been shown to beefficient [1], [7].

• Low pass filter reference microphone signals to get rid of unwanted noise.

• To keep S(z) updated continuously, evaluation of online secondary path mod-eling should be done [19].

• Implement a music compensator to improve system performance while usingan audio system simultaneously in the cabin [13].

Bibliography

[1] Akhtar M.T., Abe M., Kawamata M. (2004). Modified-filtered-x LMS algo-rithm based active noise control system with improved on line secondary-pathmodeling. Proceedings of the IEEE International Midwest Symposium on Cir-cuits and Systems, 1:I-13–I-16.

[2] Akhtar M.T., Abe M., Kawamata M. (2005). A method for on line secondarypath modeling in active noise control systems. Proceedings of the IEEE In-ternational Midwest Symposium on Circuits and Systems, 1:264–267.

[3] Arbetsmiljöverket. (2005) Buller. Arbetsmiljöverkets författningssamling,AFS 2005:16.

[4] Elliott S.J., Nelson P.A. (1993). Active Noise Control. IEEE Signal ProcessingMagazine, 10(4):12–35.

[5] Glad T., Ljung L. (2003). Reglerteori. Studentlitteratur.

[6] Gustafsson F. (2000). Adaptive Filtering and Change Detection. John Wiley& Sons, Ltd.

[7] Haweel T.I. (2004). A simple variable step size LMS adaptive algorithm. In-ternational Journal on Circuit Theory and Applications, 32:523–536.

[8] Kong X., Liu P., Kuo S.M. (1998). Multiple Channel Hybrid Active Noise Con-trol Systems. IEEE Transactions on Control Systems Technology, 6(6):719–729.

[9] Kuo S.M., Morgan D.R. (1999). Active Noise Control: A Tutorial Review.Proceedings of the IEEE, 87(6):943–973.

[10] Ljung L. (1999). System Identification, Theory for the User. 2nd edition Pren-tice Hall Information and System Sciences Series.

[11] Morgan D.R. (1980). An analysis of multiple correlation cancellation loopswith a filter in the auxiliary path. IEEE Transactions on Acoustics, Speech,and Signal Processing, 5:454–467.

[12] Pedrotti F.L., Pedrotti L.S. (1993). Introduction to Optics, 2nd edition. Pren-tice Hall International Editions.

49

50 Bibliography

[13] Sano H., Inoue T., Takahashi A., Terai K., Nakamura Y. (2001). ActiveControl System for Low-Frequency Road Noise Combined With an AudioSystem. IEEE Transactions on Speech and Audio Processing, 9(7):755–763.

[14] Streeter A.D., Ray L.R., Collier R.D. (2004). Hybrid Feedforward-FeedbackActive Noise Control. Proceedings of the 2004 American Control Conference,3:2876–2881.

[15] Texas Instruments. (2003).TMS320C6000 DSP Enhanced Direct Memory Ac-cess (EDMA) Controller Reference. SPRU234.

[16] Texas Instruments. (2004).TMS320C6000 DSP Multichannel Buffered SerialPort (McBSP) Reference Guide. SPRU580C.

[17] Texas Instruments. (2004). TMS320 DSP/BIOS User’s Guide. SPRU423D.

[18] Texas Instruments. (2006).TMS320C67x DSP Library Programmer’s Refer-ence Guide. SPRU657B.

[19] Zhang M., Lan H., Ser W. (2005). On comparison of online secondary pathmodeling methods with auxiliary noise. IEEE Transactions on Speech andAudio Processing, 13(4):618–628.

Appendix A

Equipment

Development card

One Texas instrument TMS320C6713 Development Starter Kit (DSK) is used asa signal processing hardware platform, see Figure 4.1. Key features of this cardinclude:

• A Texas Instrument floating point digital signal processor TMS320C6713operating at 225MHz.

• An AIC23 audio codec (AD/DA converter) with stereo input and output.

• 16 Mbytes of synchronous DRAM.

• 512 Kbytes of non-volatile Flash memory.

• USB communication with a host computer used for software development.

Amplifier

One four-channel Reflexion X500:

• Max output power 4 x 225 W.

• Signal to noise ratio >95 dB.

• Frequency response 10 Hz - 45 kHz.

Subwoofer

One 8 inch MDS subwoofer enclosure:

• Speaker with frequency response 24Hz - 3 kHz.

• Built in amplifier

51

52 Equipment

Preamplifier

To power the microphones an Alto Mictube stereo preamplifier is used. Thispreamplifier is connected between microphones and the DSK and has specifica-tions:

• 48 V phantom power supply to power high quality condenser microphones.

• Frequency response 20Hz - 22 kHz

Microphones

Two TW omni condenser lavaliere microphones are used.

• Frequency response 20Hz - 20 kHz.

• Omni directional, sensitive to sounds from all directionals.

One Superlux CM-H8K microphone for use in very high sound pressure environ-ments.

• Frequency response 40Hz - 20 kHz.

• Sound pressure level 134 dB.

• Omni directional, sensitive to sounds in all directions.

Mixer

One Helix board 12 FireWire mixer is used.

• 12-input small-format analog mixer with extremely low noise circuitry.

• 96 kHz FireWire interface for streaming 10 independent channels of audioto computer with near-zero latency.

• Four mono Mic/Line channels

• Two stereo channels, two stereo AUX returns, two AUX sends.

• Inserts and phantom power on Mic channels.

• Mini stereo and RCA.

UpphovsrättDetta dokument hålls tillgängligt på Internet — eller dess framtida ersättare —under 25 år från publiceringsdatum under förutsättning att inga extraordinäraomständigheter uppstår.

Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner,skriva ut enstaka kopior för enskilt bruk och att använda det oförändrat för icke-kommersiell forskning och för undervisning. Överföring av upphovsrätten vid ensenare tidpunkt kan inte upphäva detta tillstånd. All annan användning av doku-mentet kräver upphovsmannens medgivande. För att garantera äktheten, säker-heten och tillgängligheten finns det lösningar av teknisk och administrativ art.

Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsmani den omfattning som god sed kräver vid användning av dokumentet på ovan be-skrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan formeller i sådant sammanhang som är kränkande för upphovsmannens litterära ellerkonstnärliga anseende eller egenart.

För ytterligare information om Linköping University Electronic Press se för-lagets hemsida http://www.ep.liu.se/

CopyrightThe publishers will keep this document online on the Internet — or its possi-ble replacement — for a period of 25 years from the date of publication barringexceptional circumstances.

The online availability of the document implies a permanent permission foranyone to read, to download, to print out single copies for his/her own use andto use it unchanged for any non-commercial research and educational purpose.Subsequent transfers of copyright cannot revoke this permission. All other uses ofthe document are conditional on the consent of the copyright owner. The publisherhas taken technical and administrative measures to assure authenticity, securityand accessibility.

According to intellectual property law the author has the right to be mentionedwhen his/her work is accessed as described above and to be protected againstinfringement.

For additional information about the Linköping University Electronic Pressand its procedures for publication and for assurance of document integrity, pleaserefer to its www home page: http://www.ep.liu.se/

c© Patrik GrylinMårten Hedborg

Documents

Department of Electrical Engineering - DiVA portal23741/FULLTEXT01.pdf · Department of Electrical Engineering Examensarbete ... the driver’s cabin of a Valmet 890 forest machine