Acoustic Echo Cancellation (AEC) - MATLAB & Simulink Example

20/10/2015 Acoustic Echo Cancellation (AEC) MATLAB & Simulink Example

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Acoustic Echo Cancellation (AEC)

This example shows how to apply adaptive filters to acoustic echo cancellation (AEC).

Author(s): Scott C. Douglas

Introduction

The Room Impulse Response

The NearEnd Speech Signal

The FarEnd Speech Signal

The Microphone Signal

The FrequencyDomain Adaptive Filter (FDAF)

Echo Return Loss Enhancement (ERLE)

Effects of Different Step Size Values

Echo Return Loss Enhancement Comparison

IntroductionAcoustic echo cancellation is important for audio teleconferencing when simultaneous communication (or fullduplextransmission) of speech is necessary. In acoustic echo cancellation, a measured microphone signal d(n) contains twosignals:

the nearend speech signal v(n)

the farend echoed speech signal dhat(n)

The goal is to remove the farend echoed speech signal from the microphone signal so that only the nearend speechsignal is transmitted. This example has some sound clips, so you might want to adjust your computer's volume now.

The Room Impulse ResponseFirst, we describe the acoustics of the loudspeakertomicrophone signal path where the speakerphone is located. Wecan use a long finite impulse response filter to describe these characteristics. The following sequence of commandsgenerates a random impulse response that is not unlike what a conference room would exhibit assuming a systemsampling rate of fs = 16000 Hz.

fs = 16000;M = fs/2 + 1;frameSize = 8192;

[B,A] = cheby2(4,20,[0.1 0.7]);IIR = dsp.IIRFilter('Numerator', [zeros(1,6) B], 'Denominator', A);

FVT = fvtool(IIR); % Analyze the filterFVT.Color = [1 1 1];

matlab:edit adaptecdemo



H = step(IIR, ... (log(0.99*rand(1,M)+0.01).*sign(randn(1,M)).*exp(‐0.002*(1:M)))');H = H/norm(H)*4; % Room Impulse ResponsefirRoom = dsp.FIRFilter('Numerator', H');

fig = figure;plot(0:1/fs:0.5, H);xlabel('Time [sec]');ylabel('Amplitude');title('Room Impulse Response');fig.Color = [1 1 1];



The NearEnd Speech SignalThe teleconferencing system's user is typically located near the system's microphone. Here is what a male speechsounds like at the microphone.

load nearspeech

AP = dsp.AudioPlayer('SampleRate', fs);nearSpeechSrc = dsp.SignalSource('Signal',v,'SamplesPerFrame',frameSize);nearSpeechScope = dsp.TimeScope('SampleRate', fs, ... 'TimeSpan', 35, ... 'YLimits', [‐1.5 1.5], ... 'BufferLength', length(v), ... 'Title', 'Near‐End Speech Signal', ... 'ShowGrid', true);

% Stream processing loopwhile(~isDone(nearSpeechSrc)) % Extract the speech samples from the input signal nearSpeech = step(nearSpeechSrc); % Send the speech samples to the output audio device step(AP, nearSpeech); % Plot the signal step(nearSpeechScope, nearSpeech);end



The FarEnd Speech SignalNow we describe the path of the farend speech signal. A male voice travels out the loudspeaker, bounces around inthe room, and then is picked up by the system's microphone. Let's listen to what his speech sounds like if it is picked upat the microphone without the nearend speech present.



load farspeechfarSpeechSrc = dsp.SignalSource('Signal',x,'SamplesPerFrame',frameSize);farSpeechSink = dsp.SignalSink;farSpeechScope = dsp.TimeScope('SampleRate', fs, ... 'TimeSpan', 35, ... 'YLimits', [‐0.5 0.5], ... 'BufferLength', length(x), ... 'Title', 'Far‐End Speech Signal', ... 'ShowGrid', true);

% Stream processing loopwhile(~isDone(farSpeechSrc)) % Extract the speech samples from the input signal farSpeech = step(farSpeechSrc); % Add the room effect to the far‐end speech signal farSpeechEcho = step(firRoom, farSpeech); % Send the speech samples to the output audio device step(AP, farSpeechEcho); % Plot the signal step(farSpeechScope, farSpeech); % Log the signal for further processing step(farSpeechSink, farSpeechEcho);end

The Microphone Signal



The signal at the microphone contains both the nearend speech and the farend speech that has been echoedthroughout the room. The goal of the acoustic echo canceler is to cancel out the farend speech, such that only thenearend speech is transmitted back to the farend listener.

reset(nearSpeechSrc);farSpeechEchoSrc = dsp.SignalSource('Signal', farSpeechSink.Buffer, ... 'SamplesPerFrame', frameSize);micSink = dsp.SignalSink;micScope = dsp.TimeScope('SampleRate', fs,... 'TimeSpan', 35, ... 'YLimits', [‐1 1], ... 'BufferLength', length(x), ... 'Title', 'Microphone Signal', ... 'ShowGrid', true);

% Stream processing loopwhile(~isDone(farSpeechEchoSrc)) % Microphone signal = echoed far‐end + near‐end + noise micSignal = step(farSpeechEchoSrc) + step(nearSpeechSrc) + ... 0.001*randn(frameSize,1); % Send the speech samples to the output audio device step(AP, micSignal); % Plot the signal step(micScope, micSignal); % Log the signal step(micSink, micSignal);end



The FrequencyDomain Adaptive Filter (FDAF)The algorithm that we will use in this example is the FrequencyDomain Adaptive Filter (FDAF). This algorithm is veryuseful when the impulse response of the system to be identified is long. The FDAF uses a fast convolution technique tocompute the output signal and filter updates. This computation executes quickly in MATLAB®. It also has improvedconvergence performance through frequencybin step size normalization. We'll pick some initial parameters for thefilter and see how well the farend speech is cancelled in the error signal.



% Construct the Frequency‐Domain Adaptive FilterFDAF = dsp.FrequencyDomainAdaptiveFilter('Length', 2048, ... 'StepSize', 0.025, ... 'InitialPower', 0.01, ... 'AveragingFactor', 0.98, ... 'Method', 'Unconstrained FDAF');

AECScope1 = dsp.TimeScope(4, fs, ... 'LayoutDimensions', [4,1], ... 'TimeSpan', 35, ... 'BufferLength', length(x));

AECScope1.ActiveDisplay = 1;AECScope1.ShowGrid = true;AECScope1.YLimits = [‐1.5 1.5];AECScope1.Title = 'Near‐End Speech Signal';

AECScope1.ActiveDisplay = 2;AECScope1.ShowGrid = true;AECScope1.YLimits = [‐1.5 1.5];AECScope1.Title = 'Microphone Signal';

AECScope1.ActiveDisplay = 3;AECScope1.ShowGrid = true;AECScope1.YLimits = [‐1.5 1.5];AECScope1.Title = 'Output of Acoustic Echo Canceller mu=0.025';

AECScope1.ActiveDisplay = 4;AECScope1.ShowGrid = true;AECScope1.YLimits = [0 50];AECScope1.YLabel = 'ERLE [dB]';AECScope1.Title = 'Echo Return Loss Enhancement mu=0.025';

% Near‐end speech signalrelease(nearSpeechSrc);nearSpeechSrc.SamplesPerFrame = frameSize;

% Far‐end speech signalrelease(farSpeechSrc);farSpeechSrc.SamplesPerFrame = frameSize;

% Far‐end speech signal echoed by the roomrelease(farSpeechEchoSrc);farSpeechEchoSrc.SamplesPerFrame = frameSize;

Echo Return Loss Enhancement (ERLE)Since we have access to both the nearend and farend speech signals, we can compute the echo return lossenhancement (ERLE), which is a smoothed measure of the amount (in dB) that the echo has been attenuated. Fromthe plot, we see that we have achieved about a 35 dB ERLE at the end of the convergence period.



firERLE1 = dsp.FIRFilter('Numerator', ones(1,1024));firERLE2 = clone(firERLE1);setfilter(FVT,firERLE1);

micSrc = dsp.SignalSource('Signal', micSink.Buffer, ... 'SamplesPerFrame', frameSize);

% Stream processing loop ‐ adaptive filter step size = 0.025while(~isDone(nearSpeechSrc)) nearSpeech = step(nearSpeechSrc); farSpeech = step(farSpeechSrc); farSpeechEcho = step(farSpeechEchoSrc); micSignal = step(micSrc); % Apply FDAF [y,e] = step(FDAF, farSpeech, micSignal); % Send the speech samples to the output audio device step(AP, e); % Compute ERLE erle = step(firERLE1,(e‐nearSpeech).^2)./ ... (step(firERLE2, farSpeechEcho.^2)); erledB = ‐10*log10(erle); % Plot near‐end, far‐end, microphone, AEC output and ERLE step(AECScope1, nearSpeech, micSignal, e, erledB);end



Effects of Different Step Size ValuesTo get faster convergence, we can try using a larger step size value. However, this increase causes another effect, thatis, the adaptive filter is "misadjusted" while the nearend speaker is talking. Listen to what happens when we choose astep size that is 60% larger than before.



% Change the step size value in FDAFreset(FDAF);FDAF.StepSize = 0.04;

AECScope2 = clone(AECScope1);AECScope2.ActiveDisplay = 3;AECScope2.Title = 'Output of Acoustic Echo Canceller mu=0.04';AECScope2.ActiveDisplay = 4;AECScope2.Title = 'Echo Return Loss Enhancement mu=0.04';

reset(nearSpeechSrc);reset(farSpeechSrc);reset(farSpeechEchoSrc);reset(micSrc);reset(firERLE1);reset(firERLE2);

% Stream processing loop ‐ adaptive filter step size = 0.04while(~isDone(nearSpeechSrc)) nearSpeech = step(nearSpeechSrc); farSpeech = step(farSpeechSrc); farSpeechEcho = step(farSpeechEchoSrc); micSignal = step(micSrc); % Apply FDAF [y,e] = step(FDAF, farSpeech, micSignal); % Send the speech samples to the output audio device step(AP, e); % Compute ERLE erle = step(firERLE1,(e‐nearSpeech).^2)./ ... (step(firERLE2, farSpeechEcho.^2)); erledB = ‐10*log10(erle); % Plot near‐end, far‐end, microphone, AEC output and ERLE step(AECScope2, nearSpeech, micSignal, e, erledB);end



Echo Return Loss Enhancement ComparisonWith a larger step size, the ERLE performance is not as good due to the misadjustment introduced by the nearendspeech. To deal with this performance difficulty, acoustic echo cancellers include a detection scheme to tell when nearend speech is present and lower the step size value over these periods. Without such detection schemes, theperformance of the system with the larger step size is not as good as the former, as can be seen from the ERLE plots.

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Acoustic Echo Cancellation (AEC) - MATLAB & Simulink Example