ec_matlab.rar_EC回声消除_nlms adaptive_回声消除_自适应滤波算法

%% 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. fs = 8000; % x = wavread('far.wav'); % d = wavread('near.wav'); % v = wavread('near_origin.wav'); % x = wavread('1126_far.wav'); % d = wavread('1126_near.wav'); % v = wavread('1126_near_origin.wav'); x = wavread('F.wav'); d = wavread('N.wav'); v = wavread('N_origin.wav'); M = 32; % NLMS adaptive filter order mu = 0.8; %0.2; % Normalized LMS step size. Hadapt = adaptfilt.nlms(M,mu,1); % 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))); % Hadapt = adaptfilt.fdaf(2048,mu,1,del,lam); [y, e] = filter(Hadapt,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(4,1,1); plot(t,x(n),'g'); axis([0 20 -1 1]); ylabel('Amplitude'); title('Far-End Speech Signal'); subplot(4,1,2); plot(t,d(n),'b'); axis([0 20 -0.2 0.2]); ylabel('Amplitude'); title('Near-End Speech Signal'); subplot(4,1,3); plot(t,e(n),'r'); axis([0 20 -0.2 0.2]); xlabel('Time [sec]'); ylabel('Amplitude'); title('Output of Acoustic Echo Canceller'); set(gcf, 'Color', [1 1 1]) % wavwrite(e, 'out.wav'); % wavwrite(e, '1126_out.wav'); wavwrite(e, 'O.wav'); %% 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)); erle = filter(Hd2,(e-v(1:length(e))).^2)./(filter(Hd2,(d(1:length(e))-v(1:length(e))).^2)); erledB = -10*log10(erle); subplot(4,1,4); plot(t,erledB); set(gca,'YTick',[0 3 6 9 12 15 30]); grid on; axis([0 20 0 30]); xlabel('Time [sec]'); ylabel('ERLE [dB]'); title('Echo Return Loss Enhancement'); set(gcf, 'Color', [1 1 1])