Started code for DSP final project

2024-04-25 18:38:09 -04:00
parent 824a46b1fd
commit 50a5e57e18
96 changed files with 2160495 additions and 28 deletions
--- a/6th-Semester-Spring-2024/DSP/Labs/FinalProject/statistical_based/stsa_weuclid.m
+++ b/6th-Semester-Spring-2024/DSP/Labs/FinalProject/statistical_based/stsa_weuclid.m
@@ -0,0 +1,145 @@
+function stsa_weuclid(filename,outfile,p)
+
+%
+%  Implements the  Bayesian estimator based on the weighted-Euclidean
+%  distortion measure [1, Eq. 18].
+% 
+%  Usage:  stsa_weuclid(noisyFile, outputFile, p)
+%           
+%         infile - noisy speech file in .wav format
+%         outputFile - enhanced output file in .wav format
+%         p  - power exponent used in the weighted-Euclidean measure.
+%              Valid values for p: p>-2
+%  
+%
+%  Example call:  stsa_weuclid('sp04_babble_sn10.wav','out_weuclid.wav',-1);
+%
+%  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<3
+    fprintf('Usage: stsa_weuclid(infile.wav,outfile.wav,p) \n');
+    fprintf('       where p>-2 \n\n');
+    return;
+end;
+
+if p<-2,
+    error('ERROR!  p needs to be larger than -2.\n\n');
+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=hamming(len); %tukey(len,PERC);  % define window
+
+
+% Noise magnitude calculations - assuming that the first 6 frames is noise/silence
+%
+nFFT=2*len;
+nFFT2=len/2;
+noise_mean=zeros(nFFT,1);
+j=1;
+for k=1:6
+    noise_mean=noise_mean+abs(fft(win.*x(j:j+len-1),nFFT));
+    j=j+len;
+end
+noise_mu=noise_mean/6;
+noise_mu2=noise_mu.^2;
+
+%--- allocate memory and initialize various variables
+
+k=1;
+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;
+mu=0.98;
+eta=0.15;
+c=sqrt(pi)/2;
+C2=gamma(0.5);
+
+%p=-1;
+CC=gamma((p+3)/2)/gamma(p/2+1);
+ksi_min=10^(-25/10);
+
+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
+    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
+        ksi=max(ksi_min,ksi);  % limit ksi to -25 dB
+    end
+
+    log_sigma_k= gammak.* ksi./ (1+ ksi)- log(1+ ksi);
+    vad_decision= sum( log_sigma_k)/ len;    
+    if (vad_decision< eta) 
+        % noise only frame found
+        noise_mu2= mu* noise_mu2+ (1- mu)* sig2;
+    end
+    % ===end of vad===
+
+    vk=ksi.*gammak./(1+ksi);
+
+    %----- weighted Euclidean distance ------------------------
+    if p==-1
+            hw=CC*sqrt(vk)./(gammak.*exp(-vk/2).*besseli(0,vk/2));  % if p=-1 use this equation as it's faster
+    else
+            numer=CC*sqrt(vk).*confhyperg(-(p+1)/2,1,-vk,100);
+            denom=gammak.*confhyperg(-p/2,1,-vk,100);
+            hw=numer./denom;
+    end
+    %
+
+    sig=sig.*hw;
+    Xk_prev=sig.^2;
+
+    xi_w= ifft( hw .* spec, nFFT);
+    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);
+
+
+    k=k+len2;
+end
+%========================================================================================
+
+
+wavwrite(xfinal,Srate,16,outfile);
+