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8/19/2019 Aec Filter Design Toolbox

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Filter Design Toolbox

Acoust ic Echo Cancellation (AEC)

This demonstration illustrates the application of adaptive filters to acoustic echo cancellation (AEC).

Author(s): Scott C. Douglas

Contents

Introduction

The Room Impulse Response

The Near-End Speech Signal

The Far-End Speech Signal

The Microphone Signal

The Frequency-Domain Adaptive Filter (FDAF)

Echo Return Loss Enhancement (ERLE)

Effects of Different Step Size Values

Echo Return Loss Enhancement Comparison

Introduction

Acoustic echo cancellation is important for audio teleconferencing when simultaneous communication (or full-duplex

transmission) of speech is necessary. In acoustic echo cancellation, a measured microphone signal d(n) contains two signals:

- the near-end speech signal v(n) - the far-end echoed speech signal dhat(n) The goal is to remove the far-end echoed

speech signal from the microphone signal so that only the near-end speech signal is transmitted. This demo has some sound

clips, so you might want to adjust your computer's volume now.

The Room Impulse Response

First, we describe the acoustics of the loudspeaker-to-microphone signal path where the speakerphone is located. We can

use a long finite impulse response filter to describe these characteristics. The following sequence of commands generates a

random impulse response that is not unlike what a conference room would exhibit assuming a system sampling rate of fs =

8000 Hz.

M = 4001;

f s = 8000;

[ B, A] = cheby2( 4, 20, [ 0. 1 0. 7] ) ;

Hd = df i l t . df 2t ( [ zeros( 1, 6) B] , A) ;


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hFVT = f vtool ( Hd) ; % Anal yze t he f i l t er

set ( hFVT, ' Col or ' , [ 1 1 1] )

H = f i l t er ( Hd, l og( 0. 99*r and( 1, M) +0. 01) . * . . .

si gn( r andn( 1, M) ) . *exp( - 0. 002*( 1: M) ) ) ;

H = H/ norm( H) *4; % RoomI mpul se Response

pl ot ( 0: 1/ f s :0. 5, H) ;

xl abel ( ' Ti me [ sec] ' ) ;

yl abel ( ' Ampl i t ude' ) ;

t i t l e( ' Room I mpul se Response' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

The Near-End Speech Signal


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The teleconferencing system's user is typically located near the system's microphone. Here is what a male speech sounds

like at the microphone.

l oad near speech

n = 1: l engt h( v) ;

t = n/ f s;

pl ot ( t , v) ;

axi s([ 0 33. 5 - 1 1] ) ;



t i t l e( ' Near - End Speech Si gnal ' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

p8 = audi opl ayer ( v, f s) ;

pl aybl ocki ng( p8) ;

The Far-End Speech Signal

Now we describe the path of the far-end speech signal. A male voice travels out the loudspeaker, bounces around in the

room, and then is picked up by the system's microphone. Let's listen to what his speech sounds like if it is picked up at the

microphone without the near-end speech present.

l oad f arspeech

x = x( 1: l engt h( x) ) ;


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dhat = f i l t er( H, 1, x) ;

pl ot ( t , dhat ) ;

axi s([ 0 33. 5 - 1 1] ) ;



t i t l e( ' Far- End Echoed Speech Si gnal ' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

p8 = audi opl ayer ( dhat , f s) ;


The Microphone Signal

The signal at the microphone contains both the near-end speech and the far-end speech that has been echoed throughout

the room. The goal of the acoustic echo canceler is to cancel out the far-end speech, such that only the near-end speech is

transmitted back to the far-end listener.

d = dhat + v+0. 001*r andn( l ength( v) , 1) ;

pl ot ( t , d) ;

axi s([ 0 33. 5 - 1 1] ) ;



t i t l e( ' Mi cr ophone Si gnal ' ) ;


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set ( gcf , ' Col or ' , [ 1 1 1] )

p8 = audi opl ayer ( d, f s) ;


The Frequency-Domain Adaptive Filter (FDAF)

The algorithm that we will use in this demonstration is the Frequency-Domain Adaptive Filter (FDAF). This algorithm is very

useful when the impulse response of the system to be identified is long. The FDAF uses a fast convolution technique to

compute the output signal and filter updates. This computation executes quickly in MATLAB®. It also has improved

convergence performance through frequency-bin step size normalization. We'll pick some initial parameters for the filter and

see how well the far-end speech is cancelled in the error signal.

mu = 0. 025;

W0 = zer os( 1, 2048) ;

del = 0. 01;

l am = 0. 98;

x = x( 1: l engt h( W0) *f l oor ( l engt h( x)/ l engt h( W0) ) ) ;

d = d( 1: l engt h( W0) *f l oor ( l engt h( d) / l engt h( W0) ) ) ;

% Const r uct t he Frequency- Domai n Adapt i ve Fi l t er

hFDAF = adapt f i l t . f daf ( 2048, mu, 1, del , l am) ;

[ y, e] = f i l t er ( hFDAF, x, d) ;


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n = 1: l engt h( e) ;

t = n/ f s;

pos = get ( gcf , ' Pos i t i on' ) ;

set ( gcf , ' Pos i t i on' , [ pos( 1) , pos( 2) - 100, pos( 3) , ( pos( 4) +85) ] )

subpl ot ( 3, 1, 1) ;

pl ot ( t , v(n) , ' g' ) ;

axi s([ 0 33. 5 - 1 1] ) ;



subpl ot ( 3, 1, 2) ;

pl ot ( t , d(n) , ' b' ) ;

axi s([ 0 33. 5 - 1 1] ) ;


t i t l e( ' Mi cr ophone Si gnal ' ) ;

subpl ot ( 3, 1, 3) ;

pl ot ( t , e(n) , ' r ' ) ;

axi s([ 0 33. 5 - 1 1] ) ;



t i t l e( ' Out put of Acoust i c Echo Cancel l er ' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

p8 = audi opl ayer ( e/ max(abs( e) ) , f s) ;



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Echo Return Loss Enhancement (ERLE)

Since we have access to both the near-end and far-end speech signals, we can compute the echo return loss enhancement

(ERLE), which is a smoothed measure of the amount (in dB) that the echo has been attenuated. From the plot, we see that

we have achieved about a 30 dB ERLE at the end of the convergence period.

Hd2 = df i l t . df f i r ( ones( 1, 1000) ) ;

set f i l t er( hFVT, Hd2) ;

er l e = f i l t er( Hd2, ( e- v( 1: l engt h( e) ) ) . 2̂) . / . . .

( f i l t er ( Hd2, dhat ( 1: l engt h( e) ) . 2̂) ) ;

er l edB = - 10*l og10( er l e) ;


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pl ot ( t , er l edB) ;

axi s( [ 0 33. 5 0 40] ) ;


yl abel ( ' ERLE [ dB] ' ) ;

t i t l e( ' Echo Ret urn Loss Enhancement ' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

Effects o f Different Step Size Values

To get faster convergence, we can try using a larger step size value. However, this increase causes another effect, that is,

the adaptive filter is "mis-adjusted" while the near-end speaker is talking. Listen to what happens when we choose a step

size that is 60\% larger than before

newmu = 0. 04;

set ( hFDAF, ' St epSi ze' , newmu) ;

[ y, e2] = f i l t er ( hFDAF, x, d) ;

pos = get ( gcf , ' Pos i t i on' ) ;

set ( gcf , ' Pos i t i on' , [ pos( 1) , pos( 2) - 100, pos( 3) , ( pos( 4) +85) ] )

subpl ot ( 3, 1, 1) ;

pl ot ( t , v(n) , ' g' ) ;


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axi s([ 0 33. 5 - 1 1] ) ;



subpl ot ( 3, 1, 2) ;

pl ot ( t , e(n) , ' r ' ) ;

axi s([ 0 33. 5 - 1 1] ) ;


t i t l e( ' Out put of Acoust i c Echo Cancel l er , \ mu = 0. 025' ) ;

subpl ot ( 3, 1, 3) ;

pl ot ( t , e2( n) , ' r ' ) ;

axi s([ 0 33. 5 - 1 1] ) ;



t i t l e( ' Out put of Acoust i c Echo Cancel l er , \ mu = 0. 04' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

p8 = audi opl ayer ( e2/ max( abs( e2) ) , f s) ;



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Echo Return Loss Enhancement Comparison

With a larger step size, the ERLE performance is not as good due to the misadjustment introduced by the near-end speech.To deal with this performance difficulty, acoustic echo cancellers include a detection scheme to tell when near-end speech is

present and lower the step size value over these periods. Without such detection schemes, the performance of the system

with the larger step size is not as good as the former, as can be seen from the ERLE plots.

cl ose;

er l e2 = f i l t er ( Hd2, ( e2- v( 1: l engt h( e2) ) ) . 2̂) . / . . .

( f i l t er ( Hd2, dhat ( 1: l engt h( e2) ) . 2̂) ) ;

er l e2dB = - 10*l og10( er l e2) ;

pl ot ( t , [ er l edB er l e2dB] ) ;

axi s( [ 0 33. 5 0 40] ) ;


yl abel ( ' ERLE [ dB] ' ) ;


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t i t l e( ' Echo Ret urn Loss Enhancement s' ) ;

l egend( ' FDAF, \ mu = 0. 025' , ' FDAF, \ mu = 0. 04' ) ;

set ( gcf , ' Col or ' , [ 1 1 1] )

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