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comp_fwseg_variant.m
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comp_fwseg_variant.m
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function [SIG,BAK,OVL]= comp_fwseg_variant(cleanFile, enhancedFile);
% ----------------------------------------------------------------------
% Frequency-variant fwSNRseg Objective Speech Quality Measure
%
% This function implements the frequency-variant fwSNRseg measure [1]
% (see also Chap. 10, Eq. 10.24)
%
%
% Usage: [sig,bak,ovl]=comp_fwseg_variant(cleanFile.wav, enhancedFile.wav)
%
% cleanFile.wav - clean input file in .wav format
% enhancedFile - enhanced output file in .wav format
% sig - predicted rating [1-5] of speech distortion
% bak - predicted rating [1-5] of noise distortion
% ovl - predicted rating [1-5] of overall quality
%
%
% Example call: [s,b,o] =comp_fwseg_variant('sp04.wav','enhanced.wav')
%
%
% References:
% [1] S. R. Quackenbush, T. P. Barnwell, and M. A. Clements,
% Objective Measures of Speech Quality. Prentice Hall
% Advanced Reference Series, Englewood Cliffs, NJ, 1988,
% ISBN: 0-13-629056-6.
%
% Author: Philipos C. Loizou
% (critical-band filtering routines were written by Bryan Pellom & John Hansen)
%
% Copyright (c) 2006 by Philipos C. Loizou
% $Revision: 0.0 $ $Date: 10/09/2006 $
% ----------------------------------------------------------------------
if nargin~=2
fprintf('USAGE: [sig,bak,ovl]=comp_fwseg_variant(cleanFile.wav, enhancedFile.wav)\n');
fprintf('For more help, type: help comp_fwseg_variant\n\n');
return;
end
[data1, Srate1, Nbits1]= wavread(cleanFile);
[data2, Srate2, Nbits2]= wavread(enhancedFile);
if ( Srate1~= Srate2) | ( Nbits1~= Nbits2)
error( 'The two files do not match!\n');
end
len= min( length( data1), length( data2));
data1= data1( 1: len)+eps;
data2= data2( 1: len)+eps;
wss_dist_matrix= fwseg( data1, data2,Srate1);
wss_dist=mean(wss_dist_matrix);
% initialize coefficients obtained from multiple linear
% regression analysis
%
b_sig=[0.021,-0.028,0.088,-0.031,0.048,-0.049,0.065,0.009,0.011,0.033,...
-0.040,-0.002,0.041,-0.007,0.033,0.018,-0.007,0.044,-0.001,0.021,...
-0.002,0.017,-0.03,0.073,0.043];
b_ovl=[-0.003,-0.026,0.066,-0.036,0.038,-0.023,0.037,0.022,0.014,0.009,...
-0.03,0.004,0.044,-0.005,0.017,0.018,-0.001,0.051,0.009,0.011,...
0.011,-0.002,-0.021,0.043,0.031];
b_bak=[-0.03,-0.022,0.03,-0.048,0.034,0.002,0.006,0.037,0.017,-0.016,-0.008,...
0.019,0.024,-0.002,0.01,0.03,-0.018,0.046,0.022,0.005,0.03,-0.028,...
-0.028,0.019,0.005];
SIG=0.567+sum(b_sig.*wss_dist);
SIG=max(1,SIG); SIG=min(5, SIG); % limit values to [1, 5]
BAK=1.013+sum(b_bak.*wss_dist);
BAK=max(1,BAK); BAK=min(5, BAK); % limit values to [1, 5]
OVL=0.446+sum(b_ovl.*wss_dist);
OVL=max(1,OVL); OVL=min(5, OVL); % limit values to [1, 5]
% ----------------------------------------------------------------------
function distortion = fwseg(clean_speech, processed_speech,sample_rate)
% ----------------------------------------------------------------------
% Check the length of the clean and processed speech. Must be the same.
% ----------------------------------------------------------------------
clean_length = length(clean_speech);
processed_length = length(processed_speech);
if (clean_length ~= processed_length)
disp('Error: Files must have same length.');
return
end
% ----------------------------------------------------------------------
% Global Variables
% ----------------------------------------------------------------------
winlength = round(30*sample_rate/1000); % window length in samples
skiprate = floor(winlength/4); % window skip in samples
max_freq = sample_rate/2; % maximum bandwidth
num_crit = 25; % number of critical bands
n_fft = 2^nextpow2(2*winlength);
n_fftby2 = n_fft/2; % FFT size/2
% ----------------------------------------------------------------------
% Critical Band Filter Definitions (Center Frequency and Bandwidths in Hz)
% ----------------------------------------------------------------------
cent_freq(1) = 50.0000; bandwidth(1) = 70.0000;
cent_freq(2) = 120.000; bandwidth(2) = 70.0000;
cent_freq(3) = 190.000; bandwidth(3) = 70.0000;
cent_freq(4) = 260.000; bandwidth(4) = 70.0000;
cent_freq(5) = 330.000; bandwidth(5) = 70.0000;
cent_freq(6) = 400.000; bandwidth(6) = 70.0000;
cent_freq(7) = 470.000; bandwidth(7) = 70.0000;
cent_freq(8) = 540.000; bandwidth(8) = 77.3724;
cent_freq(9) = 617.372; bandwidth(9) = 86.0056;
cent_freq(10) = 703.378; bandwidth(10) = 95.3398;
cent_freq(11) = 798.717; bandwidth(11) = 105.411;
cent_freq(12) = 904.128; bandwidth(12) = 116.256;
cent_freq(13) = 1020.38; bandwidth(13) = 127.914;
cent_freq(14) = 1148.30; bandwidth(14) = 140.423;
cent_freq(15) = 1288.72; bandwidth(15) = 153.823;
cent_freq(16) = 1442.54; bandwidth(16) = 168.154;
cent_freq(17) = 1610.70; bandwidth(17) = 183.457;
cent_freq(18) = 1794.16; bandwidth(18) = 199.776;
cent_freq(19) = 1993.93; bandwidth(19) = 217.153;
cent_freq(20) = 2211.08; bandwidth(20) = 235.631;
cent_freq(21) = 2446.71; bandwidth(21) = 255.255;
cent_freq(22) = 2701.97; bandwidth(22) = 276.072;
cent_freq(23) = 2978.04; bandwidth(23) = 298.126;
cent_freq(24) = 3276.17; bandwidth(24) = 321.465;
cent_freq(25) = 3597.63; bandwidth(25) = 346.136;
bw_min = bandwidth (1); % minimum critical bandwidth
% ----------------------------------------------------------------------
% Set up the critical band filters. Note here that Gaussianly shaped
% filters are used. Also, the sum of the filter weights are equivalent
% for each critical band filter. Filter less than -30 dB and set to
% zero.
% ----------------------------------------------------------------------
min_factor = exp (-30.0 / (2.0 * 2.303)); % -30 dB point of filter
for i = 1:num_crit
f0 = (cent_freq (i) / max_freq) * (n_fftby2);
all_f0(i) = floor(f0);
bw = (bandwidth (i) / max_freq) * (n_fftby2);
norm_factor = log(bw_min) - log(bandwidth(i));
j = 0:1:n_fftby2-1;
crit_filter(i,:) = exp (-11 *(((j - floor(f0)) ./bw).^2) + norm_factor);
crit_filter(i,:) = crit_filter(i,:).*(crit_filter(i,:) > min_factor);
end
% ----------------------------------------------------------------------
% For each frame of input speech, calculate the Weighted Spectral
% Slope Measure
% ----------------------------------------------------------------------
num_frames = floor(clean_length/skiprate-(winlength/skiprate)); % number of frames
start = 1; % starting sample
window = 0.5*(1 - cos(2*pi*(1:winlength)'/(winlength+1)));
distortion=zeros(num_frames,num_crit);
for frame_count = 1:num_frames
% ----------------------------------------------------------
% (1) Get the Frames for the test and reference speech.
% Multiply by Hanning Window.
% ----------------------------------------------------------
clean_frame = clean_speech(start:start+winlength-1);
processed_frame = processed_speech(start:start+winlength-1);
clean_frame = clean_frame.*window;
processed_frame = processed_frame.*window;
% ----------------------------------------------------------
% (2) Compute the magnitude Spectrum of Clean and Processed
% ----------------------------------------------------------
clean_spec = abs(fft(clean_frame,n_fft));
processed_spec = abs(fft(processed_frame,n_fft));
% normalize so that spectra have unit area ----
clean_spec=clean_spec/sum(clean_spec(1:n_fftby2));
processed_spec=processed_spec/sum(processed_spec(1:n_fftby2));
% ----------------------------------------------------------
% (3) Compute Filterbank Output Energies (in dB scale)
% ----------------------------------------------------------
clean_energy=zeros(1,num_crit);
processed_energy=zeros(1,num_crit);
error_energy=zeros(1,num_crit);
for i = 1:num_crit
clean_energy(i) = sum(clean_spec(1:n_fftby2) ...
.*crit_filter(i,:)');
processed_energy(i) = sum(processed_spec(1:n_fftby2) ...
.*crit_filter(i,:)');
error_energy(i)=max((clean_energy(i)-processed_energy(i))^2,eps);
end
SNRlog=10*log10((clean_energy.^2)./error_energy);
distortion(frame_count,:)=min(max(SNRlog,-10),35);
start = start + skiprate;
end