Started code for DSP final project

6th-Semester-Spring-2024/DSP/Labs/FinalProject/statistical_based/stsa_wlr.m

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+function stsa_wlr(filename,outfile)
+
+%
+%  Implements the Bayesian estimator based on the weighted likelihood ratio
+%  distortion measure [1, Eq. 37].
+% 
+%  Usage:  stsa_wlr(noisyFile, outputFile)
+%           
+%         infile - noisy speech file in .wav format
+%         outputFile - enhanced output file in .wav format
+%  
+%
+%  Example call:  stsa_wlr('sp04_babble_sn10.wav','out_wlr.wav');
+%
+%  References:
+%   [1] Loizou, P. (2005). Speech enhancement based on perceptually motivated 
+%       Bayesian estimators of the speech magnitude spectrum. IEEE Trans. on Speech 
+%       and Audio Processing, 13(5), 857-869.
+%   
+% Author: Philipos C. Loizou
+%
+% Copyright (c) 2006 by Philipos C. Loizou
+% $Revision: 0.0 $  $Date: 10/09/2006 $
+%-------------------------------------------------------------------------
+
+if nargin<2
+   fprintf('Usage: stsa_wlr inFile outFile.wav \n\n');
+   return;
+end
+
+[x, Srate, bits]= wavread( filename);	
+
+
+% =============== Initialize variables ===============
+%
+len=floor(20*Srate/1000); % Frame size in samples
+if rem(len,2)==1, len=len+1; end;
+PERC=50; % window overlap in percent of frame size
+len1=floor(len*PERC/100);
+len2=len-len1; 
+
+
+win=hanning(len); %tukey(len,PERC);  % define window
+
+
+% Noise magnitude calculations - assuming that the first 6 frames is noise/silence
+%
+nFFT=len;
+nFFT2=len/2;
+noise_mean=zeros(nFFT,1);
+j=1;
+for k=1:5
+   noise_mean=noise_mean+abs(fft(win.*x(j:j+len-1),nFFT));
+   j=j+len;
+end
+noise_mu=noise_mean/5;
+noise_mu2=noise_mu.^2;
+
+%--- allocate memory and initialize various variables
+   
+img=sqrt(-1);
+x_old=zeros(len1,1);
+Nframes=floor(length(x)/len2)-1;
+xfinal=zeros(Nframes*len2,1);
+xinterv=0.001:0.01:10;
+k=1;
+aa=0.98;
+
+%===============================  Start Processing =======================================================
+%
+fprintf('This might take some time ...\n')
+for n=1:Nframes 
+   
+  
+   insign=win.*x(k:k+len-1);
+    
+   %--- Take fourier transform of  frame
+   
+   spec=fft(insign,nFFT);   
+   sig=abs(spec); % compute the magnitude
+   sig2=sig.^2;
+   
+       gammak=min(sig2./noise_mu2,40);  % post SNR. Limit it to avoid overflows
+              
+       if n==1
+           ksi=aa+(1-aa)*max(gammak-1,0);
+       else
+           ksi=aa*Xk_prev./noise_mu2 + (1-aa)*max(gammak-1,0);     % a priori SNR   
+       end
+     
+       vk=ksi.*gammak./(1+ksi);
+      
+       
+       xx=solve_wlr(vk,gammak,sig,xinterv); % solves Eq. 37 in [1]
+       
+       sig_hat=xx;
+       Xk_prev=sig_hat.^2;
+       
+       xi_w= ifft( sig_hat.* exp(img*angle(spec))); 
+	   xi_w= real( xi_w);
+	  
+      
+	% --- Overlap and add ---------------
+    %
+    xfinal(k:k+ len2-1)= x_old+ xi_w(1:len1);
+	x_old= xi_w(len1+ 1: len);
+
+    if rem(n,20)==0, fprintf('Frame: %d Percent completed:%4.2f \n',n,n*100/Nframes); end;
+    
+ k=k+len2;
+end
+%========================================================================================
+
+
+
+
+wavwrite(xfinal,Srate,16,outfile);
+
+
+%==========================================================================
+function x=solve_wlr(vk,gammak,Yk,xx);
+
+% solves non-linear Eq. 37 in [1]
+%
+
+Len=length(vk);
+L2=Len/2+1;
+
+lk05=sqrt(vk).*Yk./gammak;
+Ex=gamma(1.5)*lk05.*confhyperg(-0.5,1,-vk,100);
+Elogx=1-0.5*(2*log(lk05)+log(vk)+expint(vk));
+
+x=zeros(Len,1);
+  
+for n=1:L2
+
+    a=Elogx(n);
+    b=Ex(n);
+    ff=sprintf('log(x)+%f - %f/x',a,b);
+    y=log(xx)+a-b./xx;
+    bet=xx(1); tox=200;
+    if y(1)<0
+            ind=find(y>0);
+            bet=xx(1)/2;
+            tox=xx(ind(1));
+             
+            [x(n),fval,flag]=fzero(inline(ff),[bet tox]);
+            if flag<0
+                x(n)=x(n-1);
+           end
+    else
+        ind=find(y<0);
+        if ~isempty(ind)
+            bet=xx(1);
+            tox=xx(ind(1));
+            [x(n),fval]=fzero(inline(ff),[bet tox]);
+            
+        else
+   
+            x(n)=0.001;  % spectral floor
+   
+        end
+    end    
+       
+
+end
+
+x(L2+1:Len)=flipud(x(2:L2-1));
+