先贴代码，有空再写推倒，具体使用场景是把混杂的多个人的声音分离出来，在语音识别方面多见
代码语言：javascript复制I1 = wavread('speechmixture1.wav');
I2 = wavread('speechmixture2.wav');
I3 = wavread('speechmixture3.wav');
I4 = wavread('speechmixture4.wav');
subplot(4,3,1),plot(I1),title('Input1');
subplot(4,3,2),plot(I2),title('Input2');
subplot(4,3,3),plot(I3),title('Input3');
subplot(4,3,3),plot(I4),title('Input4');
% 将其组成矩阵
II1 = I1';
II2 = I2';
II3 = I3';
II4 = I4';
S=[II1;II2;II3;II4];
Sweight=rand(size(S,1));
MixedS=Sweight*S; % 将混合矩阵重新排列并输出
subplot(4,3,4),plot(MixedS(1,:)),title('Input1');
subplot(4,3,5),plot(MixedS(2,:)),title('Input2');
subplot(4,3,6),plot(MixedS(3,:)),title('Input3');
subplot(4,3,6),plot(MixedS(4,:)),title('Input4');
MixedS_bak=MixedS;
MixedS_mean=zeros(3,1);
for i=1:4
MixedS_mean(i)=mean(MixedS(i,:));
end
for i=1:4
for j=1:size(MixedS,2)
MixedS(i,j)=MixedS(i,j)-MixedS_mean(i);
end
end
MixedS_cov=cov(MixedS');
[E,D]=eig(MixedS_cov);
Q=inv(sqrt(D))*(E)';
MixedS_white=Q*MixedS;
IsI=cov(MixedS_white');
X=MixedS_white;
[VariableNum,SampleNum]=size(X);
numofIC=VariableNum;
B=zeros(numofIC,VariableNum);
for r=1:numofIC
i=1;maxIterationsNum=100;
IterationsNum=0;
b=rand(numofIC,1)-.5;
b=b/norm(b);
while i=maxIterationsNum 1
if i == maxIterationsNum
fprintf('n第%d分量在%d次迭代内并不收敛。', r,maxIterationsNum);
break;
end
bOld=b;
a2=1;
u=1;
t=X'*b;
g=t.*exp(-a2*t.^2/2);
dg=(1-a2*t.^2).*exp(-a2*t.^2/2);
b=((1-u)*t'*g*b u*X*g)/SampleNum-mean(dg)*b;
b=b-B*B'*b;
b=b/norm(b);
if abs(abs(b'*bOld)-1)<1e-9
B(:,r)=b;
break;
end
i=i 1;
end
% B(:,r)=b;
end
ICAedS=B'*Q*MixedS_bak;
subplot(4,3,7),plot(ICAedS(1,:)),title('ICA解混信号1');
subplot(4,3,8),plot(ICAedS(2,:)),title('ICA解混信号2');
subplot(4,3,9),plot(ICAedS(3,:)),title('ICA解混信号3');
subplot(4,3,9),plot(ICAedS(4,:)),title('ICA解混信号4');
%保存wav
% wavwrite(ICAedS(1,:),8000,8,'ica_speechmixture1.wav');
% wavwrite(ICAedS(2,:),8000,8,'ica_speechmixture2.wav');
% wavwrite(ICAedS(3,:),8000,8,'ica_speechmixture3.wav');
% wavwrite(ICAedS(3,:),8000,8,'ica_speechmixture4.wav');
