222 lines
8.4 KiB
Matlab
222 lines
8.4 KiB
Matlab
function [SIG,BAK,OVL]= comp_fwseg_variant(cleanFile, enhancedFile);
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% ----------------------------------------------------------------------
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% Frequency-variant fwSNRseg Objective Speech Quality Measure
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%
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% This function implements the frequency-variant fwSNRseg measure [1]
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% (see also Chap. 10, Eq. 10.24)
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%
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%
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% Usage: [sig,bak,ovl]=comp_fwseg_variant(cleanFile.wav, enhancedFile.wav)
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%
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% cleanFile.wav - clean input file in .wav format
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% enhancedFile - enhanced output file in .wav format
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% sig - predicted rating [1-5] of speech distortion
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% bak - predicted rating [1-5] of noise distortion
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% ovl - predicted rating [1-5] of overall quality
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%
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%
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% Example call: [s,b,o] =comp_fwseg_variant('sp04.wav','enhanced.wav')
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%
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%
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% References:
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% [1] S. R. Quackenbush, T. P. Barnwell, and M. A. Clements,
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% Objective Measures of Speech Quality. Prentice Hall
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% Advanced Reference Series, Englewood Cliffs, NJ, 1988,
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% ISBN: 0-13-629056-6.
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%
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% Author: Philipos C. Loizou
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% (critical-band filtering routines were written by Bryan Pellom & John Hansen)
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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~=2
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fprintf('USAGE: [sig,bak,ovl]=comp_fwseg_variant(cleanFile.wav, enhancedFile.wav)\n');
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fprintf('For more help, type: help comp_fwseg_variant\n\n');
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return;
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end
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[data1, Srate1, Nbits1]= wavread(cleanFile);
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[data2, Srate2, Nbits2]= wavread(enhancedFile);
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if ( Srate1~= Srate2) | ( Nbits1~= Nbits2)
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error( 'The two files do not match!\n');
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end
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len= min( length( data1), length( data2));
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data1= data1( 1: len)+eps;
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data2= data2( 1: len)+eps;
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wss_dist_matrix= fwseg( data1, data2,Srate1);
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wss_dist=mean(wss_dist_matrix);
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% initialize coefficients obtained from multiple linear
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% regression analysis
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%
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b_sig=[0.021,-0.028,0.088,-0.031,0.048,-0.049,0.065,0.009,0.011,0.033,...
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-0.040,-0.002,0.041,-0.007,0.033,0.018,-0.007,0.044,-0.001,0.021,...
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-0.002,0.017,-0.03,0.073,0.043];
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b_ovl=[-0.003,-0.026,0.066,-0.036,0.038,-0.023,0.037,0.022,0.014,0.009,...
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-0.03,0.004,0.044,-0.005,0.017,0.018,-0.001,0.051,0.009,0.011,...
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0.011,-0.002,-0.021,0.043,0.031];
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b_bak=[-0.03,-0.022,0.03,-0.048,0.034,0.002,0.006,0.037,0.017,-0.016,-0.008,...
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0.019,0.024,-0.002,0.01,0.03,-0.018,0.046,0.022,0.005,0.03,-0.028,...
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-0.028,0.019,0.005];
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SIG=0.567+sum(b_sig.*wss_dist);
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SIG=max(1,SIG); SIG=min(5, SIG); % limit values to [1, 5]
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BAK=1.013+sum(b_bak.*wss_dist);
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BAK=max(1,BAK); BAK=min(5, BAK); % limit values to [1, 5]
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OVL=0.446+sum(b_ovl.*wss_dist);
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OVL=max(1,OVL); OVL=min(5, OVL); % limit values to [1, 5]
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% ----------------------------------------------------------------------
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function distortion = fwseg(clean_speech, processed_speech,sample_rate)
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% ----------------------------------------------------------------------
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% Check the length of the clean and processed speech. Must be the same.
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% ----------------------------------------------------------------------
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clean_length = length(clean_speech);
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processed_length = length(processed_speech);
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if (clean_length ~= processed_length)
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disp('Error: Files must have same length.');
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return
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end
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% ----------------------------------------------------------------------
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% Global Variables
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% ----------------------------------------------------------------------
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winlength = round(30*sample_rate/1000); % window length in samples
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skiprate = floor(winlength/4); % window skip in samples
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max_freq = sample_rate/2; % maximum bandwidth
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num_crit = 25; % number of critical bands
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n_fft = 2^nextpow2(2*winlength);
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n_fftby2 = n_fft/2; % FFT size/2
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% ----------------------------------------------------------------------
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% Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz)
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% ----------------------------------------------------------------------
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cent_freq(1) = 50.0000; bandwidth(1) = 70.0000;
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cent_freq(2) = 120.000; bandwidth(2) = 70.0000;
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cent_freq(3) = 190.000; bandwidth(3) = 70.0000;
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cent_freq(4) = 260.000; bandwidth(4) = 70.0000;
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cent_freq(5) = 330.000; bandwidth(5) = 70.0000;
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cent_freq(6) = 400.000; bandwidth(6) = 70.0000;
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cent_freq(7) = 470.000; bandwidth(7) = 70.0000;
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cent_freq(8) = 540.000; bandwidth(8) = 77.3724;
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cent_freq(9) = 617.372; bandwidth(9) = 86.0056;
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cent_freq(10) = 703.378; bandwidth(10) = 95.3398;
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cent_freq(11) = 798.717; bandwidth(11) = 105.411;
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cent_freq(12) = 904.128; bandwidth(12) = 116.256;
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cent_freq(13) = 1020.38; bandwidth(13) = 127.914;
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cent_freq(14) = 1148.30; bandwidth(14) = 140.423;
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cent_freq(15) = 1288.72; bandwidth(15) = 153.823;
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cent_freq(16) = 1442.54; bandwidth(16) = 168.154;
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cent_freq(17) = 1610.70; bandwidth(17) = 183.457;
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cent_freq(18) = 1794.16; bandwidth(18) = 199.776;
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cent_freq(19) = 1993.93; bandwidth(19) = 217.153;
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cent_freq(20) = 2211.08; bandwidth(20) = 235.631;
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cent_freq(21) = 2446.71; bandwidth(21) = 255.255;
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cent_freq(22) = 2701.97; bandwidth(22) = 276.072;
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cent_freq(23) = 2978.04; bandwidth(23) = 298.126;
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cent_freq(24) = 3276.17; bandwidth(24) = 321.465;
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cent_freq(25) = 3597.63; bandwidth(25) = 346.136;
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bw_min = bandwidth (1); % minimum critical bandwidth
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% ----------------------------------------------------------------------
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% Set up the critical band filters. Note here that Gaussianly shaped
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% filters are used. Also, the sum of the filter weights are equivalent
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% for each critical band filter. Filter less than -30 dB and set to
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% zero.
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% ----------------------------------------------------------------------
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min_factor = exp (-30.0 / (2.0 * 2.303)); % -30 dB point of filter
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for i = 1:num_crit
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f0 = (cent_freq (i) / max_freq) * (n_fftby2);
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all_f0(i) = floor(f0);
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bw = (bandwidth (i) / max_freq) * (n_fftby2);
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norm_factor = log(bw_min) - log(bandwidth(i));
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j = 0:1:n_fftby2-1;
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crit_filter(i,:) = exp (-11 *(((j - floor(f0)) ./bw).^2) + norm_factor);
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crit_filter(i,:) = crit_filter(i,:).*(crit_filter(i,:) > min_factor);
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end
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% ----------------------------------------------------------------------
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% For each frame of input speech, calculate the Weighted Spectral
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% Slope Measure
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% ----------------------------------------------------------------------
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num_frames = floor(clean_length/skiprate-(winlength/skiprate)); % number of frames
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start = 1; % starting sample
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window = 0.5*(1 - cos(2*pi*(1:winlength)'/(winlength+1)));
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distortion=zeros(num_frames,num_crit);
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for frame_count = 1:num_frames
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% ----------------------------------------------------------
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% (1) Get the Frames for the test and reference speech.
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% Multiply by Hanning Window.
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% ----------------------------------------------------------
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clean_frame = clean_speech(start:start+winlength-1);
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processed_frame = processed_speech(start:start+winlength-1);
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clean_frame = clean_frame.*window;
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processed_frame = processed_frame.*window;
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% ----------------------------------------------------------
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% (2) Compute the magnitude Spectrum of Clean and Processed
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% ----------------------------------------------------------
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clean_spec = abs(fft(clean_frame,n_fft));
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processed_spec = abs(fft(processed_frame,n_fft));
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% normalize so that spectra have unit area ----
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clean_spec=clean_spec/sum(clean_spec(1:n_fftby2));
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processed_spec=processed_spec/sum(processed_spec(1:n_fftby2));
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% ----------------------------------------------------------
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% (3) Compute Filterbank Output Energies (in dB scale)
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% ----------------------------------------------------------
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clean_energy=zeros(1,num_crit);
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processed_energy=zeros(1,num_crit);
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error_energy=zeros(1,num_crit);
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for i = 1:num_crit
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clean_energy(i) = sum(clean_spec(1:n_fftby2) ...
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.*crit_filter(i,:)');
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processed_energy(i) = sum(processed_spec(1:n_fftby2) ...
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.*crit_filter(i,:)');
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error_energy(i)=max((clean_energy(i)-processed_energy(i))^2,eps);
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end
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SNRlog=10*log10((clean_energy.^2)./error_energy);
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distortion(frame_count,:)=min(max(SNRlog,-10),35);
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start = start + skiprate;
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end
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