ecan.rar_Echo Echo_MATLAB eCan_echo_echo cancellation_echo canc

clear all; mule = .01; % Larger values for fast conv max_run = 200; for run=1:max_run; taps = 20; %Adaptive Filter Taps # freq = 2000;%Signal Freq w = zeros(1,taps);%state of adaptive filter time = .2;%lenght of simulation (sec) samplerate = 8000;%samples/sec samples = time*samplerate; max_iterations = samples-taps+1; iterations = 1:max_iterations;%Vector of iterations t=1/samplerate:1/samplerate:time; rand('state',sum(100*clock));%Reset Randome Generator noise=.02*rand(1,samples);%noise added to signal s=.4*sin(2*pi*freq*t);%Pure Signal x=noise+s;%input to adaptive filter echo_amp_per = .4; %Echo percent of signal %rand('state',sum(100*clock));%Reset Randome Generator echo_time_delay = .064; echo_delay=echo_time_delay*samplerate; echo = echo_amp_per*[zeros(1,echo_delay) x(echo_delay+1:samples)]; %LMS for i=1:max_iterations; y(i)=w*x(i:i+taps-1)'; e(run,i)=echo(i)-y(i); %mule(i) = .5/(x(i:i+taps-1)*x(i:i+taps-1)'+ .01); w = w + 2*mule*e(run,i)*x(i:i+taps-1); end end %%Mean Square Error mse=sum(e.^2,1)/max_run; b=x+echo; %Ouput of System out=b(1:length(y))-y; subplot(3,1,1),plot(b); title('Signal and Echo'); ylabel('Amp'); xlabel('Time sec'); subplot(3,1,2),plot(out); title('Output of System'); ylabel('Amp'); xlabel('Time sec'); subplot(3,1,3),semilogy(mse); grid title('LEARNING CURVE mu=.01 echo delay=64ms runs=200'); ylabel('Estimated MSE, dB'); xlabel('Number of Iterations'); %subplot(3,1,2),semilogy(iterations,e(1,:).^2); %grid %subplot(3,1,3),semilogy(iterations,e(2,:).^2); %grid