Started code for DSP final project

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

@@ -0,0 +1,126 @@
+function wiener_as(filename,outfile)
+
+%
+%  Implements the Wiener filtering algorithm based on a priori SNR estimation [1].
+% 
+%  Usage:  wiener_as(noisyFile, outputFile)
+%           
+%         infile - noisy speech file in .wav format
+%         outputFile - enhanced output file in .wav format
+
+%         
+%  Example call:  wiener_as('sp04_babble_sn10.wav','out_wien_as.wav');
+%
+%  References:
+%   [1] Scalart, P. and Filho, J. (1996). Speech enhancement based on a priori 
+%       signal to noise estimation. Proc. IEEE Int. Conf. Acoust. , Speech, Signal 
+%       Processing, 629-632.
+%   
+% Authors: Yi Hu and Philipos C. Loizou
+%
+% Copyright (c) 2006 by Philipos C. Loizou
+% $Revision: 0.0 $  $Date: 10/09/2006 $
+%-------------------------------------------------------------------------
+
+if nargin<2
+   fprintf('Usage: wiener_as(noisyfile.wav,outFile.wav) \n\n');
+   return;
+end
+
+
+
+[noisy_speech, fs]= audioread( filename);
+noisy_speech= noisy_speech; 
+% column vector noisy_speech
+
+% set parameter values
+mu= 0.98; % smoothing factor in noise spectrum update
+a_dd= 0.98; % smoothing factor in priori update
+eta= 0.15; % VAD threshold
+frame_dur= 20; % frame duration 
+L= frame_dur* fs/ 1000; % L is frame length (160 for 8k sampling rate)
+hamming_win= hamming( L); % hamming window
+U= ( hamming_win'* hamming_win)/ L; % normalization factor
+
+% first 120 ms is noise only
+len_120ms= fs/ 1000* 120;
+% first_120ms= noisy_speech( 1: len_120ms).* ...
+%     (hann( len_120ms, 'periodic'))';
+first_120ms= noisy_speech( 1: len_120ms);
+
+% =============now use Welch's method to estimate power spectrum with
+% Hamming window and 50% overlap
+nsubframes= floor( len_120ms/ (L/ 2))- 1;  % 50% overlap
+noise_ps= zeros( L, 1);
+n_start= 1; 
+for j= 1: nsubframes
+    noise= first_120ms( n_start: n_start+ L- 1);
+    noise= noise.* hamming_win;
+    noise_fft= fft( noise, L);
+    noise_ps= noise_ps+ ( abs( noise_fft).^ 2)/ (L* U);
+    n_start= n_start+ L/ 2; 
+end
+noise_ps= noise_ps/ nsubframes;
+%==============
+
+% number of noisy speech frames 
+len1= L/ 2; % with 50% overlap
+nframes= floor( length( noisy_speech)/ len1)- 1; 
+n_start= 1; 
+
+for j= 1: nframes
+    noisy= noisy_speech( n_start: n_start+ L- 1);
+    noisy= noisy.* hamming_win;
+    noisy_fft= fft( noisy, L);
+    noisy_ps= ( abs( noisy_fft).^ 2)/ (L* U);
+    
+    % ============ voice activity detection
+    if (j== 1) % initialize posteri
+        posteri= noisy_ps./ noise_ps;
+        posteri_prime= posteri- 1; 
+        posteri_prime( find( posteri_prime< 0))= 0;
+        priori= a_dd+ (1-a_dd)* posteri_prime;
+    else
+        posteri= noisy_ps./ noise_ps;
+        posteri_prime= posteri- 1;
+        posteri_prime( find( posteri_prime< 0))= 0;
+        priori= a_dd* (G_prev.^ 2).* posteri_prev+ ...
+            (1-a_dd)* posteri_prime;
+    end
+
+    log_sigma_k= posteri.* priori./ (1+ priori)- log(1+ priori);    
+    vad_decision(j)= sum( log_sigma_k)/ L;    
+    if (vad_decision(j)< eta) 
+        % noise only frame found
+        noise_ps= mu* noise_ps+ (1- mu)* noisy_ps;
+        vad( n_start: n_start+ L- 1)= 0;
+    else
+        vad( n_start: n_start+ L- 1)= 1;
+    end
+    % ===end of vad===
+    
+    G= sqrt( priori./ (1+ priori)); % gain function
+   
+    enhanced= ifft( noisy_fft.* G, L);
+        
+    if (j== 1)
+        enhanced_speech( n_start: n_start+ L/2- 1)= ...
+            enhanced( 1: L/2);
+    else
+        enhanced_speech( n_start: n_start+ L/2- 1)= ...
+            overlap+ enhanced( 1: L/2);  
+    end
+    
+    overlap= enhanced( L/ 2+ 1: L);
+    n_start= n_start+ L/ 2; 
+    
+    G_prev= G; 
+    posteri_prev= posteri;
+    
+end
+
+enhanced_speech( n_start: n_start+ L/2- 1)= overlap; 
+
+audiowrite(outfile,enhanced_speech,fs,'BitsPerSample',16);
+
+