%% Acoustic Echo Cancellation (AEC) % This demonstration illustrates the application of adaptive filters to% acoustic echo cancellation (AEC).  % % Author(s): Scott C. Douglas% Copyright 1999-2005 The MathWorks, Inc.%% 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;  fs = 8000;[B,A] = cheby2(4,20,[0.1 0.7]);Hd = dfilt.df2t([zeros(1,6) B],A);hFVT = fvtool(Hd);  % Analyze the filterset(hFVT, 'Color', [1 1 1])%%H = filter(Hd,log(0.99*rand(1,M)+0.01).* ...    sign(randn(1,M)).*exp(-0.002*(1:M)));H = H/norm(H)*4;    % Room Impulse Responseplot(0:1/fs:0.5,H);xlabel('Time [sec]');ylabel('Amplitude');title('Room Impulse Response');set(gcf, 'Color', [1 1 1])%% The Near-End Speech Signal%% The teleconferencing system's user is typically located near the % system's microphone.  Here is what a male speech sounds like at the % microphone.load nearspeechn = 1:length(v);t = n/fs;plot(t,v);axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Near-End Speech Signal');set(gcf, 'Color', [1 1 1])p8 = audioplayer(v,fs);playblocking(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.load farspeechx = x(1:length(x));dhat = filter(H,1,x);plot(t,dhat);axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Far-End Echoed Speech Signal');set(gcf, 'Color', [1 1 1])p8 = audioplayer(dhat,fs);playblocking(p8);%% 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*randn(length(v),1);plot(t,d);axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Microphone Signal');set(gcf, 'Color', [1 1 1])p8 = audioplayer(d,fs);playblocking(p8);%% 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 = zeros(1,2048);del = 0.01;lam = 0.98;x = x(1:length(W0)*floor(length(x)/length(W0)));d = d(1:length(W0)*floor(length(d)/length(W0)));% Construct the Frequency-Domain Adaptive FilterhFDAF = adaptfilt.fdaf(2048,mu,1,del,lam);[y,e] = filter(hFDAF,x,d);n = 1:length(e);t = n/fs;pos = get(gcf,'Position');set(gcf,'Position',[pos(1), pos(2)-100,pos(3),(pos(4)+85)])subplot(3,1,1);plot(t,v(n),'g');axis([0 33.5 -1 1]);ylabel('Amplitude');title('Near-End Speech Signal');subplot(3,1,2);plot(t,d(n),'b');axis([0 33.5 -1 1]);ylabel('Amplitude');title('Microphone Signal');subplot(3,1,3);plot(t,e(n),'r');axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Output of Acoustic Echo Canceller');set(gcf, 'Color', [1 1 1])p8 = audioplayer(e/max(abs(e)),fs);playblocking(p8);%% 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 = dfilt.dffir(ones(1,1000));setfilter(hFVT,Hd2);%%erle = filter(Hd2,(e-v(1:length(e))).^2)./ ...    (filter(Hd2,dhat(1:length(e)).^2));erledB = -10*log10(erle); plot(t,erledB);axis([0 33.5 0 40]);xlabel('Time [sec]');ylabel('ERLE [dB]');title('Echo Return Loss Enhancement');set(gcf, 'Color', [1 1 1])%% Effects of 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% beforenewmu = 0.04;set(hFDAF,'StepSize',newmu);[y,e2] = filter(hFDAF,x,d);pos = get(gcf,'Position');set(gcf,'Position',[pos(1), pos(2)-100,pos(3),(pos(4)+85)])subplot(3,1,1);plot(t,v(n),'g');axis([0 33.5 -1 1]);ylabel('Amplitude');title('Near-End Speech Signal');subplot(3,1,2);plot(t,e(n),'r');axis([0 33.5 -1 1]);ylabel('Amplitude');title('Output of Acoustic Echo Canceller, \mu = 0.025');subplot(3,1,3);plot(t,e2(n),'r');axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Output of Acoustic Echo Canceller, \mu = 0.04');set(gcf, 'Color', [1 1 1])p8 = audioplayer(e2/max(abs(e2)),fs);playblocking(p8);%% 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.close;erle2 = filter(Hd2,(e2-v(1:length(e2))).^2)./...    (filter(Hd2,dhat(1:length(e2)).^2));erle2dB = -10*log10(erle2);plot(t,[erledB erle2dB]);axis([0 33.5 0 40]);xlabel('Time [sec]');ylabel('ERLE [dB]');title('Echo Return Loss Enhancements');legend('FDAF, \mu = 0.025','FDAF, \mu = 0.04');set(gcf, 'Color', [1 1 1])displayEndOfDemoMessage(mfilename)