nlms.m

LMS算法

clear all
FS=8000;
order=10;
N=  60800;


speech=wavread('signal_noise88_1.wav',N);
voice=wavread('signal_1.wav',N);
noise=wavread('noise_low_1.wav',N);

%speech=wavread('zishiying2.wav',N);
%voice=wavread('signal_1.wav',N);
%noise=wavread('noise_low_1.wav',N);

L=length(speech);
y=zeros(1,L);
mu=0.2;
leakage=0.1;
bias=0;   %0.1;
%for N=1:10;
   step=20; 
   w=zeros(step,1);
    WW=0;
  num=L/step;
for i=2:L/step
    in=speech((i-1)*step+1:i*step);
      ref=noise((i-1)*step+1:i*step);
     %  ref=noise((i-2)*step+1:(i-1)*step);
   err=in-w.*ref;
%     w=w+mu*err.*in;
 w=(1-mu*leakage)*w+mu*err.*(ref)/(bias+ref'*ref);
    y((i-1)*step+1:i*step)=err;
end

    
% for i=order:L
%     buffered=noise(i:-1:i-order+1);
%     y(i)=w*buffered;
%     e(i)=speech(i)-y(i);
%      w=w+mu*e(i)*
%     w=(1-mu*leakage)*w+mu*e(i)*(buffered.')/(bias+buffered'*buffered);
% %   WW(i-9,1:10)=w;
%   
%     
% %end
% %speach=e';
% end

%%建立2个图象
figure(1)
subplot(3,1,1);plot(voice);title('纯净语音信号');
subplot(3,1,2);plot(speech);title('带噪语音信号');
subplot(3,1,3);;plot(y);title('过滤后的信号')
figure(2)
subplot(2,1,1);plot(noise);title('噪声信号');
subplot(2,1,2);plot(y);title('输出噪声信号');


%播放语音
pause(5)  
sound(speech)
pause(8)
sound(y)