163 lines
5.4 KiB
Matlab
163 lines
5.4 KiB
Matlab
function llr_mean= comp_llr(cleanFile, enhancedFile);
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% ----------------------------------------------------------------------
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%
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% Log Likelihood Ratio (LLR) Objective Speech Quality Measure
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%
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%
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% This function implements the Log Likelihood Ratio Measure
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% defined on page 48 of [1] (see Equation 2.18).
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%
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% Usage: llr=comp_llr(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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% llr - computed likelihood ratio
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%
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% Note that the LLR measure is limited in the range [0, 2].
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%
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% Example call: llr =comp_llr('sp04.wav','enhanced.wav')
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%
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%
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% References:
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%
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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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% Authors: Bryan L. Pellom and John H. L. Hansen (July 1998)
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% Modified by: Philipos C. Loizou (Oct 2006) - limited LLR to be in [0,2]
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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: LLR=comp_llr(cleanFile.wav, enhancedFile.wav)\n');
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fprintf('For more help, type: help comp_llr\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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IS_dist= llr( data1, data2,Srate1);
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IS_len= round( length( IS_dist)* alpha);
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IS= sort( IS_dist);
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llr_mean= mean( IS( 1: IS_len));
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function distortion = llr(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: Both Speech Files must be 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); %240; % window length in samples
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skiprate = floor(winlength/4); % window skip in samples
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if sample_rate<10000
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P = 10; % LPC Analysis Order
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else
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P=16; % this could vary depending on sampling frequency.
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end
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% ----------------------------------------------------------------------
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% For each frame of input speech, calculate the Log Likelihood Ratio
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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) Get the autocorrelation lags and LPC parameters used
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% to compute the LLR measure.
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% ----------------------------------------------------------
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[R_clean, Ref_clean, A_clean] = ...
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lpcoeff(clean_frame, P);
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[R_processed, Ref_processed, A_processed] = ...
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lpcoeff(processed_frame, P);
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% ----------------------------------------------------------
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% (3) Compute the LLR measure
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% ----------------------------------------------------------
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numerator = A_processed*toeplitz(R_clean)*A_processed';
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denominator = A_clean*toeplitz(R_clean)*A_clean';
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distortion(frame_count) = min(2,log(numerator/denominator));
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start = start + skiprate;
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end
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function [acorr, refcoeff, lpparams] = lpcoeff(speech_frame, model_order)
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% ----------------------------------------------------------
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% (1) Compute Autocorrelation Lags
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% ----------------------------------------------------------
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winlength = max(size(speech_frame));
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for k=1:model_order+1
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R(k) = sum(speech_frame(1:winlength-k+1) ...
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.*speech_frame(k:winlength));
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end
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% ----------------------------------------------------------
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% (2) Levinson-Durbin
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% ----------------------------------------------------------
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a = ones(1,model_order);
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E(1)=R(1);
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for i=1:model_order
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a_past(1:i-1) = a(1:i-1);
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sum_term = sum(a_past(1:i-1).*R(i:-1:2));
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rcoeff(i)=(R(i+1) - sum_term) / E(i);
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a(i)=rcoeff(i);
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a(1:i-1) = a_past(1:i-1) - rcoeff(i).*a_past(i-1:-1:1);
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E(i+1)=(1-rcoeff(i)*rcoeff(i))*E(i);
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end
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acorr = R;
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refcoeff = rcoeff;
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lpparams = [1 -a];
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