300 lines
11 KiB
Matlab
300 lines
11 KiB
Matlab
function wss_dist= comp_wss(cleanFile, enhancedFile);
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% ----------------------------------------------------------------------
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%
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% Weighted Spectral Slope (WSS) Objective Speech Quality Measure
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%
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% This function implements the Weighted Spectral Slope (WSS)
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% distance measure originally proposed in [1]. The algorithm
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% works by first decomposing the speech signal into a set of
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% frequency bands (this is done for both the test and reference
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% frame). The intensities within each critical band are
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% measured. Then, a weighted distances between the measured
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% slopes of the log-critical band spectra are computed.
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% This measure is also described in Section 2.2.9 (pages 56-58)
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% of [2].
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%
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% Whereas Klatt's original measure used 36 critical-band
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% filters to estimate the smoothed short-time spectrum, this
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% implementation considers a bank of 25 filters spanning
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% the 4 kHz bandwidth.
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%
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% Usage: wss_dist=comp_wss(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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% wss_dist - computed spectral slope distance
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%
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% Example call: ws =comp_wss('sp04.wav','enhanced.wav')
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%
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% References:
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%
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% [1] D. H. Klatt, "Prediction of Perceived Phonetic Distance
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% from Critical-Band Spectra: A First Step", Proc. IEEE
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% ICASSP'82, Volume 2, pp. 1278-1281, May, 1982.
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%
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% [2] 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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% Authors: Bryan L. Pellom and John H. L. Hansen (July 1998)
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% Modified by: Philipos C. Loizou (Oct 2006)
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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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% ----------------------------------------------------------------------
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if nargin~=2
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fprintf('USAGE: WSS=comp_wss(cleanFile.wav, enhancedFile.wav)\n');
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fprintf('For more help, type: help comp_wss\n\n');
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return;
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end
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alpha= 0.95;
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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_vec= wss( data1, data2,Srate1);
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wss_dist_vec= sort( wss_dist_vec);
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wss_dist= mean( wss_dist_vec( 1: round( length( wss_dist_vec)*alpha)));
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function distortion = wss(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 musthave 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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USE_FFT_SPECTRUM = 1; % defaults to 10th order LP spectrum
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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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Kmax = 20; % value suggested by Klatt, pg 1280
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Klocmax = 1; % value suggested by Klatt, pg 1280
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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 = 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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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 Power Spectrum of Clean and Processed
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% ----------------------------------------------------------
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if (USE_FFT_SPECTRUM)
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clean_spec = (abs(fft(clean_frame,n_fft)).^2);
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processed_spec = (abs(fft(processed_frame,n_fft)).^2);
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else
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a_vec = zeros(1,n_fft);
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a_vec(1:11) = lpc(clean_frame,10);
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clean_spec = 1.0/(abs(fft(a_vec,n_fft)).^2)';
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a_vec = zeros(1,n_fft);
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a_vec(1:11) = lpc(processed_frame,10);
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processed_spec = 1.0/(abs(fft(a_vec,n_fft)).^2)';
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end
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% ----------------------------------------------------------
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% (3) Compute Filterbank Output Energies (in dB scale)
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% ----------------------------------------------------------
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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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end
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clean_energy = 10*log10(max(clean_energy,1E-10));
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processed_energy = 10*log10(max(processed_energy,1E-10));
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% ----------------------------------------------------------
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% (4) Compute Spectral Slope (dB[i+1]-dB[i])
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% ----------------------------------------------------------
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clean_slope = clean_energy(2:num_crit) - ...
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clean_energy(1:num_crit-1);
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processed_slope = processed_energy(2:num_crit) - ...
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processed_energy(1:num_crit-1);
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% ----------------------------------------------------------
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% (5) Find the nearest peak locations in the spectra to
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% each critical band. If the slope is negative, we
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% search to the left. If positive, we search to the
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% right.
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% ----------------------------------------------------------
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for i = 1:num_crit-1
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% find the peaks in the clean speech signal
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if (clean_slope(i)>0) % search to the right
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n = i;
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while ((n<num_crit) & (clean_slope(n) > 0))
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n = n+1;
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end
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clean_loc_peak(i) = clean_energy(n-1);
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else % search to the left
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n = i;
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while ((n>0) & (clean_slope(n) <= 0))
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n = n-1;
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end
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clean_loc_peak(i) = clean_energy(n+1);
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end
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% find the peaks in the processed speech signal
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if (processed_slope(i)>0) % search to the right
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n = i;
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while ((n<num_crit) & (processed_slope(n) > 0))
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n = n+1;
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end
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processed_loc_peak(i) = processed_energy(n-1);
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else % search to the left
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n = i;
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while ((n>0) & (processed_slope(n) <= 0))
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n = n-1;
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end
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processed_loc_peak(i) = processed_energy(n+1);
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end
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end
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% ----------------------------------------------------------
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% (6) Compute the WSS Measure for this frame. This
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% includes determination of the weighting function.
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% ----------------------------------------------------------
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dBMax_clean = max(clean_energy);
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dBMax_processed = max(processed_energy);
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% The weights are calculated by averaging individual
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% weighting factors from the clean and processed frame.
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% These weights W_clean and W_processed should range
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% from 0 to 1 and place more emphasis on spectral
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% peaks and less emphasis on slope differences in spectral
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% valleys. This procedure is described on page 1280 of
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% Klatt's 1982 ICASSP paper.
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Wmax_clean = Kmax ./ (Kmax + dBMax_clean - ...
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clean_energy(1:num_crit-1));
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Wlocmax_clean = Klocmax ./ ( Klocmax + clean_loc_peak - ...
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clean_energy(1:num_crit-1));
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W_clean = Wmax_clean .* Wlocmax_clean;
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Wmax_processed = Kmax ./ (Kmax + dBMax_processed - ...
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processed_energy(1:num_crit-1));
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Wlocmax_processed = Klocmax ./ ( Klocmax + processed_loc_peak - ...
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processed_energy(1:num_crit-1));
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W_processed = Wmax_processed .* Wlocmax_processed;
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W = (W_clean + W_processed)./2.0;
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distortion(frame_count) = sum(W.*(clean_slope(1:num_crit-1) - ...
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processed_slope(1:num_crit-1)).^2);
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% this normalization is not part of Klatt's paper, but helps
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% to normalize the measure. Here we scale the measure by the
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% sum of the weights.
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distortion(frame_count) = distortion(frame_count)/sum(W);
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start = start + skiprate;
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end
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