Started code for DSP final project

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

@@ -0,0 +1,153 @@
+function stsa_mis(filename,outfile)
+
+%
+%  Implements the Bayesian estimator based on the modified Itakura-Saito 
+%  distortion measure [1, Eq. 43].
+% 
+%  Usage:  stsa_mis(noisyFile, outputFile)
+%           
+%         infile - noisy speech file in .wav format
+%         outputFile - enhanced output file in .wav format
+%  
+%
+%  Example call:  stsa_mis('sp04_babble_sn10.wav','out_mis.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_mis 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);
+
+%===============================  Start Processing =======================================================
+%
+k=1;
+aa=0.98;
+fprintf('\nThis 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);
+      
+       sig_hat=log(comp_int(vk,gammak,sig)); % Eq. 41
+       
+       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);
+
+%------------------------------E N D  -----------------------------------
+function xhat=comp_int(vk,gammak,Yk)
+
+% -- Evaluates Eq. 43 in [1]
+%
+
+Yk2=Yk.*Yk;
+G2=gammak.^2;
+EV=exp(-vk);
+
+N=40; % number of terms to keep in infinite sum (Eq. 43)
+L=length(vk)/2+1;
+J1=zeros(L,1);
+J2=zeros(L,1);
+
+for j=1:L
+  sum=0;  sum_b=0;
+  for m=0:N
+     F=factorial(m);
+     d1=(vk(j))^m;
+     d2=hyperg(-m,-m,0.5,Yk2(j)/(4*G2(j)),10);
+     d2_b=hyperg(-m,-m,1.5,Yk2(j)/(4*G2(j)),10);
+     sum=sum+d1*d2/F;
+     sum_b=sum_b+gamma(m+1.5)*d1*d2_b/(F*gamma(m+1));
+end
+ J1(j)=sum;
+ J2(j)=sum_b;
+end
+ 
+
+J1=J1.*EV(1:L);
+J2=J2.*EV(1:L).*sqrt(vk(1:L)).*Yk(1:L)./gammak(1:L);
+
+
+xhat2=max(real(J1+J2),0.00001);
+xhat = [xhat2; flipud(xhat2(2:L-1))];