adaptecdemo.m

自适应滤波器设计是现在滤波器设计的重要一方面

%% 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 filter
set(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 Response
plot(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 nearspeech
n = 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 farspeech
x = 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 Filter
hFDAF = 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
% before

newmu = 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)