146 lines
3.6 KiB
Matlab
146 lines
3.6 KiB
Matlab
function stsa_weuclid(filename,outfile,p)
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%
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% Implements the Bayesian estimator based on the weighted-Euclidean
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% distortion measure [1, Eq. 18].
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%
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% Usage: stsa_weuclid(noisyFile, outputFile, p)
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%
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% infile - noisy speech file in .wav format
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% outputFile - enhanced output file in .wav format
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% p - power exponent used in the weighted-Euclidean measure.
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% Valid values for p: p>-2
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%
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%
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% Example call: stsa_weuclid('sp04_babble_sn10.wav','out_weuclid.wav',-1);
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%
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% References:
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% [1] Loizou, P. (2005). Speech enhancement based on perceptually motivated
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% Bayesian estimators of the speech magnitude spectrum. IEEE Trans. on Speech
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% and Audio Processing, 13(5), 857-869.
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%
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% Author: Philipos C. Loizou
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%
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% Copyright (c) 2006 by Philipos C. Loizou
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% $Revision: 0.0 $ $Date: 10/09/2006 $
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%-------------------------------------------------------------------------
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if nargin<3
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fprintf('Usage: stsa_weuclid(infile.wav,outfile.wav,p) \n');
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fprintf(' where p>-2 \n\n');
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return;
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end;
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if p<-2,
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error('ERROR! p needs to be larger than -2.\n\n');
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end
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[x, Srate, bits]= wavread( filename);
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% =============== Initialize variables ===============
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len=floor(20*Srate/1000); % Frame size in samples
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if rem(len,2)==1, len=len+1; end;
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PERC=50; % window overlap in percent of frame size
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len1=floor(len*PERC/100);
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len2=len-len1;
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win=hamming(len); %tukey(len,PERC); % define window
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% Noise magnitude calculations - assuming that the first 6 frames is noise/silence
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%
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nFFT=2*len;
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nFFT2=len/2;
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noise_mean=zeros(nFFT,1);
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j=1;
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for k=1:6
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noise_mean=noise_mean+abs(fft(win.*x(j:j+len-1),nFFT));
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j=j+len;
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end
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noise_mu=noise_mean/6;
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noise_mu2=noise_mu.^2;
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%--- allocate memory and initialize various variables
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k=1;
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img=sqrt(-1);
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x_old=zeros(len1,1);
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Nframes=floor(length(x)/len2)-1;
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xfinal=zeros(Nframes*len2,1);
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%=============================== Start Processing =======================================================
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%
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k=1;
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aa=0.98;
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mu=0.98;
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eta=0.15;
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c=sqrt(pi)/2;
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C2=gamma(0.5);
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%p=-1;
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CC=gamma((p+3)/2)/gamma(p/2+1);
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ksi_min=10^(-25/10);
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for n=1:Nframes
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insign=win.*x(k:k+len-1);
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%--- Take fourier transform of frame
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spec=fft(insign,nFFT);
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sig=abs(spec); % compute the magnitude
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sig2=sig.^2;
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gammak=min(sig2./noise_mu2,40); % post SNR
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if n==1
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ksi=aa+(1-aa)*max(gammak-1,0);
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else
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ksi=aa*Xk_prev./noise_mu2 + (1-aa)*max(gammak-1,0); % a priori SNR
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ksi=max(ksi_min,ksi); % limit ksi to -25 dB
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end
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log_sigma_k= gammak.* ksi./ (1+ ksi)- log(1+ ksi);
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vad_decision= sum( log_sigma_k)/ len;
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if (vad_decision< eta)
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% noise only frame found
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noise_mu2= mu* noise_mu2+ (1- mu)* sig2;
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end
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% ===end of vad===
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vk=ksi.*gammak./(1+ksi);
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%----- weighted Euclidean distance ------------------------
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if p==-1
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hw=CC*sqrt(vk)./(gammak.*exp(-vk/2).*besseli(0,vk/2)); % if p=-1 use this equation as it's faster
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else
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numer=CC*sqrt(vk).*confhyperg(-(p+1)/2,1,-vk,100);
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denom=gammak.*confhyperg(-p/2,1,-vk,100);
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hw=numer./denom;
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end
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%
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sig=sig.*hw;
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Xk_prev=sig.^2;
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xi_w= ifft( hw .* spec, nFFT);
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xi_w= real( xi_w);
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% --- Overlap and add ---------------
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%
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xfinal(k:k+ len2-1)= x_old+ xi_w(1:len1);
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x_old= xi_w(len1+ 1: len);
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k=k+len2;
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end
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%========================================================================================
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wavwrite(xfinal,Srate,16,outfile);
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