语音合成与识别

开发平台：

Matlab

Basic_LMS.asv：源码内容

							clear all;
% the channel impulse response; 
Hn =[0.8783   -0.5806    0.6537   -0.3223    0.6577   -0.0582   0.2895   -0.2710    0.1278   -0.1508    0.0238   -0.1814   0.2519   -0.0396    0.0423   -0.0152    0.1664   -0.0245   0.1463   -0.0770    0.1304   -0.0148    0.0054   -0.0381    0.0374   -0.0329    0.0313   -0.0253    0.0552  -0.0369   0.0479   -0.0073    0.0305   -0.0138    0.0152   -0.0012  0.0154   -0.0092    0.0177   -0.0161    0.0070   -0.0042  0.0051   -0.0131    0.0059   -0.0041    0.0077   -0.0034   0.0074   -0.0014    0.0025   -0.0056    0.0028   -0.0005   0.0033   -0.0000    0.0022   -0.0032    0.0012   -0.0020   0.0017   -0.0022    0.0004   -0.0011      0          0   ];
Hn=Hn(1:64);
%读入语音;  it is a column vector;
r=wavread('C:MatlabworkWrite_In_PaperShi_Yan.wav');
% cut the former 5000 points;
r=r'; r=r(1:5000); r=r+0.2*randn(size(r));% r is the input noisy signal vector;
mu=0.3;
% the output signal vector;
output=conv(r,Hn);  N=length(r);
output=output(1:N);
% ss=wavread('C:MatlabworkWrite_In_PaperShi_Yan1.wav');
% ss=ss(1:N);
% output=output+sqrt(1000)*ss';
k=length(Hn);   % k is the order of the filter;
win=zeros(1,k);  % the filter coeffients vector;
error=zeros(1,N);
for i=k:N
    input=r(i:-1:i-k+1);  % intercept the input vector;
    e=output(i)-win*input';%进行滤波
    win=win+mu*e*input;%修正系数
    error(i)=error(i)+e^2;%累积误差
end;
    figure;    plot(win,'r'); hold on; plot(Hn,'b');%绘制滤波器系数和信道脉冲响应曲线
   figure;
    semilogy(error,'b');
 
    
    
    
      %axis([0  N  10^(-30) 0]);
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% Normalized LMS Algorithm   
mu=1.5;
win=zeros(1,k);  % the filter coeffients vector;
error=zeros(1,N);
for i=k:N
    input=r(i:-1:i-k+1);  % intercept the input vector;
    e=output(i)-win*input';
    win=win+mu*e*input/(3.5+input*input');
    error(i)=error(i)+e^2;
end;
    hold on;
    semilogy(error,'r');%对纵坐标取对数的误差曲线
  
    
    
      T=64;
      L=zeros(T,T);
  for j=1:T
      L(1,j)=sqrt(1/T);
  end;
  for i=2:T
      for j=1:T
          L(i,j)=sqrt(2/T)*cos((i-1)*(2*j-1)*pi/(2*T));
      end;
  end;
    win=zeros(1,k);  % the filter coeffients vector;
    error=zeros(1,N);
    l=zeros(64,1);
for i=k:N
     u=r(i:-1:i-k+1);
     for n=1:64
         l(n)=L(n,:)*u';
     end;
    input=l';
    mu=0.1;
     % intercept the input vector;
    e=output(i)-win*input';
    win=win+mu*e*input;
    error(i)=error(i)+e^2;
end;
hold on;
    semilogy(error,'y');
    
    
    
    
    
    
  

						