%%%%%%%%%%%%%%%%%%%%%%%%%% 初始化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;clear all;close all;
%%%%%%%%%%%%%% 读入原始图像，混合，并输出混合图像 %%%%%%%%%%%%%%%%%%
% 读入混合前的原始图片并显示
I1=wavread ('man.wav')';
I2=wavread ('dragen.wav')';
I3=wavread ('music.wav')';
subplot(4,3,1),plot(I1),title('输入声音1'),
subplot(4,3,2),plot(I2),title('输入声音2'),
subplot(4,3,3),plot(I3),title('输入声音3'),
% 将其组成矩阵
S=[I1;I2;I3]; % 图片个数即为变量数，图片的像素数即为采样数
% 因此S_all是一个变量个数＊采样个数的矩阵
Sweight=rand(size(S,1)); % 取一随机矩阵，作为信号混合的权矩阵
MixedS=Sweight*S; % 得到三个图像的混合信号矩阵
% 将混合矩阵重新排列并输出
subplot(4,3,4),plot(MixedS(1,:)),title('混合声音1'),
subplot(4,3,5),plot(MixedS(2,:)),title('混合声音2'),
subplot(4,3,6),plot(MixedS(3,:)),title('混合声音3'),
MixedS_bak=MixedS; % 将混合后的数据备份，以便在恢复时直接调用
%%%%%%%%%%%%%%%%%%%%%%%%%% 标准化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
MixedS_mean=zeros(3,1);
for i=1:3
MixedS_mean(i)=mean(MixedS(i,:));
end % 计算MixedS的均值
for i=1:3
for j=1:size(MixedS,2)
MixedS(i,j)=MixedS(i,j)-MixedS_mean(i);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%% 白化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
MixedS_cov=cov(MixedS'); % cov为求协方差的函数
[E,D]=eig(MixedS_cov); % 对图片矩阵的协方差函数进行特征值分解
Q=inv(sqrt(D))*(E)'; % Q为白化矩阵
MixedS_white=Q*MixedS; % MixedS_white为白化后的图片矩阵
IsI=cov(MixedS_white'); % IsI应为单位阵
%%%%%%%%%%%%%%%%%%%%%%%% FASTICA算法 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
X=MixedS_white; % 以下算法将对X进行操作
[VariableNum,SampleNum]=size(X);
numofIC=VariableNum; % 在此应用中，独立元个数等于变量个数
B=zeros(numofIC,VariableNum); % 初始化列向量w的寄存矩阵,B=[b1 b2 ... bd]
for r=1:numofIC
i=1;maxIterationsNum=100; % 设置最大迭代次数（即对于每个独立分量而言迭代均不超过此次数）
IterationsNum=0;
b=rand(numofIC,1)-.5; % 随机设置b初值
b=b/norm(b); % 对b标准化 norm(b):向量元素平方和开根号
while i<=maxIterationsNum+1
if i == maxIterationsNum % 循环结束处理
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;
% 核心公式，参见理论部分公式2.52
b=b-B*B'*b; % 对b正交化
b=b/norm(b);
if abs(abs(b'*bOld)-1)<1e-9 % 如果收敛，则
B(:,r)=b; % 保存所得向量b
break;
end
i=i+1;
end
% B(:,r)=b; % 保存所得向量b
end
%%%%%%%%%%%%%%%%%%%%%%%%%% ICA计算的数据复原并构图 %%%%%%%%%%%%%%%%%%%%%%%%%
ICAedS=B'*Q*MixedS_bak; % 计算ICA后的矩阵
% 将混合矩阵重新排列并输出
subplot(4,3,7),plot(ICAedS(1,:)),title('ICA解混声音1'),
subplot(4,3,8),plot(ICAedS(2,:)),title('ICA解混声音2'),
subplot(4,3,9),plot(ICAedS(3,:)),title('ICA解混声音3'),
%%%%%%%%%%%%%%%%%%%%%%%%%% PCA计算并构图 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[V,D]=eig(MixedS_cov); % 协方差矩阵对角化
Vtmp=zeros(size(V,1),1);
for j=1:2 % 选择最大的主元向量并排序
for i=1:2
if D(i,i)<D(i+1,i+1)
tmp=D(i,i);Vtmp=V(:,i);
D(i,i)=D(i+1,i+1);V(:,i)=V(:,i+1);
D(i+1,i+1)=tmp;V(:,i+1)=Vtmp;
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%% PCA求主元并显示 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
t1=(MixedS'*V(:,1))';
t2=(MixedS'*V(:,2))';
t3=(MixedS'*V(:,3))';
subplot(4,3,10),plot(t1),title('PCA解混声音1'),
subplot(4,3,11),plot(t2),title('PCA解混声音2'),
subplot(4,3,12),plot(t3),title('PCA解混声音3'),
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