盲源分离+JADE算法仿真+matlab仿真+语音信号分离+亲测好用

function [A,S]=jade(X,m) % Source separation of complex signals with JADE. % Jade performs `Source Separation' in the following sense: % X is an n x T data matrix assumed modelled as X = A S + N where % % o A is an unknown n x m matrix with full rank. % o S is a m x T data matrix (source signals) with the properties % a) for each t, the components of S(:,t) are statistically % independent % b) for each p, the S(p,:) is the realization of a zero-mean % `source signal'. % c) At most one of these processes has a vanishing 4th-order % cumulant. % o N is a n x T matrix. It is a realization of a spatially white % Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance % sigma. This is probably better than no modeling at all... % % Jade performs source separation via a % Joint Approximate Diagonalization of Eigen-matrices. % % THIS VERSION ASSUMES ZERO-MEAN SIGNALS % % Input : % * X: Each column of X is a sample from the n sensors % * m: m is an optional argument for the number of sources. % If ommited, JADE assumes as many sources as sensors. % % Output : % * A is an n x m estimate of the mixing matrix % * S is an m x T naive (ie pinv(A)*X) estimate of the source signals % % % Version 1.5. Copyright: JF Cardoso. % % See notes, references and revision history at the bottom of this file [n,T] = size(X); %% source detection not implemented yet ! if nargin==1, m=n ; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % A few parameters that could be adjusted nem = m; % number of eigen-matrices to be diagonalized seuil = 1/sqrt(T)/100;% a statistical threshold for stopping joint diag %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% whitening % if m<n, %assumes white noise [U,D] = eig((X*X')/T); [puiss,k]=sort(diag(D)); ibl = sqrt(puiss(n-m+1:n)-mean(puiss(1:n-m))); bl = ones(m,1) ./ ibl ; W = diag(bl)*U(1:n,k(n-m+1:n))'; IW = U(1:n,k(n-m+1:n))*diag(ibl); else %assumes no noise IW = sqrtm((X*X')/T); W = inv(IW); end; Y = W*X; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Cumulant estimation R = (Y*Y' )/T ; C = (Y*Y.')/T ; Yl = zeros(1,T); Ykl = zeros(1,T); Yjkl = zeros(1,T); Q = zeros(m*m*m*m,1) ; index = 1; for lx = 1:m ; Yl = Y(lx,:); for kx = 1:m ; Ykl = Yl.*conj(Y(kx,:)); for jx = 1:m ; Yjkl = Ykl.*conj(Y(jx,:)); for ix = 1:m ; Q(index) = ... (Yjkl * Y(ix,:).')/T - R(ix,jx)*R(lx,kx) - R(ix,kx)*R(lx,jx) - C(ix,lx)*conj(C(jx,kx)) ; index = index + 1 ; end ; end ; end ; end %% If you prefer to use more memory and less CPU, you may prefer this %% code (due to J. Galy of ENSICA) for the estimation the cumulants %ones_m = ones(m,1) ; %T1 = kron(ones_m,Y); %T2 = kron(Y,ones_m); %TT = (T1.* conj(T2)) ; %TS = (T1 * T2.')/T ; %R = (Y*Y')/T ; %Q = (TT*TT')/T - kron(R,ones(m)).*kron(ones(m),conj(R)) - R(:)*R(:)' - TS.*TS' ; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%computation and reshaping of the significant eigen matrices [U,D] = eig(reshape(Q,m*m,m*m)); [la,K] = sort(abs(diag(D))); %% reshaping the most (there are `nem' of them) significant eigenmatrice M = zeros(m,nem*m); % array to hold the significant eigen-matrices Z = zeros(m) ; % buffer h = m*m; for u=1:m:nem*m, Z(:) = U(:,K(h)); M(:,u:u+m-1) = la(h)*Z; h = h-1; end; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% joint approximate diagonalization of the eigen-matrices %% Better declare the variables used in the loop : B = [ 1 0 0 ; 0 1 1 ; 0 -i i ] ; Bt = B' ; Ip = zeros(1,nem) ; Iq = zeros(1,nem) ; g = zeros(3,nem) ; G = zeros(2,2) ; vcp = zeros(3,3); D = zeros(3,3); la = zeros(3,1); K = zeros(3,3); angles = zeros(3,1); pair = zeros(1,2); c = 0 ; s = 0 ; %init; encore = 1; V = eye(m); % Main loop while encore, encore=0; for p=1:m-1, for q=p+1:m, Ip = p:m:nem*m ; Iq = q:m:nem*m ; % Computing the Givens angles g = [ M(p,Ip)-M(q,Iq) ; M(p,Iq) ; M(q,Ip) ] ; [vcp,D] = eig(real(B*(g*g')*Bt)); [la, K] = sort(diag(D)); angles = vcp(:,K(3)); if angles(1)<0 , angles= -angles ; end ; c = sqrt(0.5+angles(1)/2); s = 0.5*(angles(2)-j*angles(3))/c; if abs(s)>seuil, %%% updates matrices M and V by a Givens rotation encore = 1 ; pair = [p;q] ; G = [ c -conj(s) ; s c ] ; V(:,pair) = V(:,pair)*G ; M(pair,:) = G' * M(pair,:) ; M(:,[Ip Iq]) = [ c*M(:,Ip)+s*M(:,Iq) -conj(s)*M(:,Ip)+c*M(:,Iq) ] ; end%% if end%% q loop end%% p loop end%% while %%%estimation of the mixing matrix and signal separation A = IW*V; S = V'*Y ; return ; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Note 1: This version does *not* assume circularly distributed % signals as 1.1 did. The difference only entails more computations % in estimating the cumulants % % % Note 2: This code tries to minimize the work load by jointly % diagonalizing only the m most significant eigenmatrices of the % cumulant tensor. When the model holds, this avoids the % diagonalization of m^2 matrices. However, when the model does not % hold, there is in general more than m significant eigen-matrices. % In this case, this code still `works' but is no longer equivalent to % the minimization of a well defined contrast function: this would % require the diagonalization of *all* the eigen-matrices. We note % (see the companion paper) that diagonalizing **all** the % eigen-matrices is strictly equivalent to diagonalize all the % `parallel cumulants slices'. In other words, when the model does % not hold, it could be a good idea to diagonalize all the parallel % cumulant slices. The joint diagonalization will require about m % times more operations, but on the other hand, computation of the % eigen-matrices is avoided. Such an approach makes sense when % dealing with a relatively small number of sources (say smaller than % 10). % % % Revision history %----------------- % % Version 1.5 (Nov. 2, 97) : % o Added the option kindly provided by Jerome Galy % (galy@dirac.ensica.fr) to compute the sample cumulant tensor. % This option uses more memory but is faster (a similar piece of % code was also passed to me by Sandip Bose). % o Suppressed the useles variable `oui'. % o Changed (angles=sign(angles(1))*angles) to (if angles(1)<0 , % angles= -angles ; end ;) as suggested by Iain Collings % <i.collings@ee.mu.OZ.AU>. This is safer (with probability 0 in % the case of sample statistics) % o Cosmetic rewriting of the doc. Fixed some typos and added new % ones. % % Version 1.4 (Oct. 9, 97) : Changed the code for estimating % cumulants. The new version loops thru the sensor indices rather than % looping thru the time index. This is much faster for large sample % sizes. Also did some clean up. One can now change the number of % eigen-matrices to be jointly diagonalized by just changing the % variable `nem'. It is still hard coded below to be equal to the % number of sources. This is more economical and OK when the model % holds but not appropriate when the model does not hold (in which % case, the algorithm is no longer asymptotically equivalent to % minimizing a contrast function, unless nem is the square of the % number of sources.) % % Version 1.3 (Oct. 6, 97) : Added various Matalb tricks to speed up % things a bit. This is not very rewarding though, because the main % computational burden still is in estimating the 4th-order moments. % % Version 1.2 (Mar