demo_bss.m

这是盲信号的代码 都已经通过编译了 做这方面的同仁可以参考一下 我觉得蛮惯用的

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% Copyright (c) by JV Stone, University of Sheffield.
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clear all;

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% SET UP
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global demo_id;

% Set rand number seeds.
seed=9;randn('state',seed);rand('state',seed);

demo_id 		= 2;	% Used by get_data, set to 1 or 2, default=2..
num_sources		= 3;
num_mixtures	= num_sources;
num_samples 	= 5000;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% GET DATA.
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% sources = (num_samples x num_sources),  
% mixtures = (num_samples x num_mixtures), 
% A = (num_sources x num_sources) mixing matrix, mixtures=sources*A;

% Get zero mean, unit variance sources.
[sources mixtures A] = get_data(num_samples);  % one mixture per column.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% COMPUTE V AND U.
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% Set short and long half-lives.
shf 		= 1; 
lhf 		= 900000; 	
max_mask_len= 500;
n			= 8; % n = num half lives to be used to make mask.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Get masks to be used to find (x_tilde-x) and (x_bar-x)
% Set mask to have -1 as first element,
% and remaining elements sum to unity.

% Short-term mask.
h=shf; t = n*h; lambda = 2^(-1/h); temp = [0:t-1]'; 
lambdas = ones(t,1)*lambda; mask = lambda.^temp;
mask(1) = 0; mask = mask/sum(abs(mask));  mask(1) = -1;
s_mask=mask; s_mask_len = length(s_mask);

% Long-term mask.
h=lhf;t = n*h; t = min(t,max_mask_len); t=max(t,1);
lambda = 2^(-1/h); temp = [0:t-1]'; 
lambdas = ones(t,1)*lambda; mask = lambda.^temp;
mask(1) = 0; mask = mask/sum(abs(mask));  mask(1) = -1;
l_mask=mask; l_mask_len = length(l_mask);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Filter each column of mixtures array.
S=filter(s_mask,1,mixtures); 	L=filter(l_mask,1,mixtures);

% Can also use this for L: L = zero_mean_cols(mixtures);

% Find short-term and long-term covariance matrices.
U=cov(S,1);		V=cov(L,1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Find eigenvectors W and eigenvalues d.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[W d]=eig(V,U); W=real(W);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Recover source signals.
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ys = mixtures*W;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PLOT RESULTS
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% Rescale ys to zero-mean and unit variance, for display purposes.
temp=repmat(mean(ys),[num_samples 1]'); 	ys=ys-temp;
temp=repmat(std(ys,1),[num_samples 1]');	ys=ys./temp;
% Plot original sources ... 
figure(1);clf;

nn=1000;
if demo_id==1 imin=100; else imin=1; end;
imin=1;imax=imin+nn-1;
figure(1);
for i=1:num_sources	
	subplot(num_sources,1,i);	
	plot(mixtures(imin:imax,i)/std(mixtures(imin:imax,i),1),'k.'); 
	axis off; 
end;
subplot(num_sources,1,1); title('Signal Mixtures');

% Plot corresponding recovered sources.
a=[sources ys]; c=corrcoef(a);  rs=c(1:num_sources,num_sources+1:num_sources*2);
fprintf('Correlations between sources and recovered signals ...\n'); abs(rs)

rs_bin=abs(rs)>0.9; figure(2);
for i=1:num_sources
	subplot(num_sources,1,i); hold off;
	plot(-1+sources(imin:imax,i),'k'); hold on;
	% Find corresponding sources/ys via correlation matrix rs_bin.
	j=find(rs_bin(i,:)); s=sign(rs(i,j));
	plot(2+s*ys(imin:imax,j),'r--'); hold off; axis off;
end;

subplot(num_sources,1,1);
title('Source signal (bottom trace) and recovered signal (top trace)');

% Play sounds
if demo_id==1
	fprintf('Playing mixtures ...\n');pause(2);
	for i=1:num_sources
		soundsc(mixtures(:,i));pause(1);
	end;
	
	fprintf('Playing recovered signals ...\n');pause(2);
	for i=1:num_sources
		soundsc(ys(:,i));pause(1);
	end;
end;
	
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