v_specsubm

PURPOSE ^

V_SPECSUBM obsolete speech enhancement algorithm - use v_specsub instead

SYNOPSIS ^

function [ss,po]=v_specsubm(s,fs,p)

DESCRIPTION ^

V_SPECSUBM obsolete speech enhancement algorithm - use v_specsub instead

 implementation of spectral subtraction algorithm by R Martin (rather slow)
 algorithm parameters: t* in seconds, f* in Hz, k* dimensionless
 1: tg = smoothing time constant for signal power estimate (0.04): high=reverberant, low=musical
 2: ta = smoothing time constant for signal power estimate
        used in noise estimation (0.1)
 3: tw = fft window length (will be rounded up to 2^nw samples)
 4: tm = length of minimum filter (1.5): high=slow response to noise increase, low=distortion
 5: to = time constant for oversubtraction factor (0.08)
 6: fo = oversubtraction corner frequency (800): high=distortion, low=musical
 7: km = number of minimisation buffers to use (4): high=waste memory, low=noise modulation
 8: ks = oversampling constant (4)
 9: kn = noise estimate compensation (1.5)
 10:kf = subtraction floor (0.02): high=noisy, low=musical
 11:ko = oversubtraction scale factor (4): high=distortion, low=musical

 Refs:
    (a) R. Martin. Spectral subtraction based on minimum statistics. In Proc EUSIPCO, pages 1182-1185, Edinburgh, Sept 1994.
    (b) R. Martin. Noise power spectral density estimation based on optimal smoothing and minimum statistics.
        IEEE Trans. Speech and Audio Processing, 9(5):504-512, July 2001.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function [ss,po]=v_specsubm(s,fs,p)
0002 %V_SPECSUBM obsolete speech enhancement algorithm - use v_specsub instead
0003 %
0004 % implementation of spectral subtraction algorithm by R Martin (rather slow)
0005 % algorithm parameters: t* in seconds, f* in Hz, k* dimensionless
0006 % 1: tg = smoothing time constant for signal power estimate (0.04): high=reverberant, low=musical
0007 % 2: ta = smoothing time constant for signal power estimate
0008 %        used in noise estimation (0.1)
0009 % 3: tw = fft window length (will be rounded up to 2^nw samples)
0010 % 4: tm = length of minimum filter (1.5): high=slow response to noise increase, low=distortion
0011 % 5: to = time constant for oversubtraction factor (0.08)
0012 % 6: fo = oversubtraction corner frequency (800): high=distortion, low=musical
0013 % 7: km = number of minimisation buffers to use (4): high=waste memory, low=noise modulation
0014 % 8: ks = oversampling constant (4)
0015 % 9: kn = noise estimate compensation (1.5)
0016 % 10:kf = subtraction floor (0.02): high=noisy, low=musical
0017 % 11:ko = oversubtraction scale factor (4): high=distortion, low=musical
0018 %
0019 % Refs:
0020 %    (a) R. Martin. Spectral subtraction based on minimum statistics. In Proc EUSIPCO, pages 1182-1185, Edinburgh, Sept 1994.
0021 %    (b) R. Martin. Noise power spectral density estimation based on optimal smoothing and minimum statistics.
0022 %        IEEE Trans. Speech and Audio Processing, 9(5):504-512, July 2001.
0023 
0024 
0025 %      Copyright (C) Mike Brookes 2004
0026 %      Version: $Id: v_specsubm.m 10865 2018-09-21 17:22:45Z dmb $
0027 %
0028 %   VOICEBOX is a MATLAB toolbox for speech processing.
0029 %   Home page: http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
0030 %
0031 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0032 %   This program is free software; you can redistribute it and/or modify
0033 %   it under the terms of the GNU General Public License as published by
0034 %   the Free Software Foundation; either version 2 of the License, or
0035 %   (at your option) any later version.
0036 %
0037 %   This program is distributed in the hope that it will be useful,
0038 %   but WITHOUT ANY WARRANTY; without even the implied warranty of
0039 %   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
0040 %   GNU General Public License for more details.
0041 %
0042 %   You can obtain a copy of the GNU General Public License from
0043 %   http://www.gnu.org/copyleft/gpl.html or by writing to
0044 %   Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA.
0045 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0046 
0047 if nargin<3 po=[0.04 0.1 0.032 1.5 0.08 400 4 4 1.5 0.02 4].'; else po=p; end
0048 ns=length(s);
0049 ts=1/fs;
0050 ss=zeros(ns,1);
0051 
0052 ni=pow2(nextpow2(fs*po(3)/po(8)));
0053 ti=ni/fs;
0054 nw=ni*po(8);
0055 nf=1+floor((ns-nw)/ni);
0056 nm=ceil(fs*po(4)/(ni*po(7)));
0057 
0058 win=0.5*hamming(nw+1)/1.08;win(end)=[];
0059 zg=exp(-ti/po(1));
0060 za=exp(-ti/po(2));
0061 zo=exp(-ti/po(5));
0062 
0063 px=zeros(1+nw/2,1);
0064 pxn=px;
0065 os=px;
0066 mb=ones(1+nw/2,po(7))*nw/2;
0067 im=0;
0068 osf=po(11)*(1+(0:nw/2).'*fs/(nw*po(6))).^(-1);
0069 
0070 imidx=[13 21]';
0071 x2im=zeros(length(imidx),nf);
0072 osim=x2im;
0073 pnim=x2im;
0074 pxnim=x2im;
0075 qim=x2im;
0076 
0077 for is=1:nf
0078    idx=(1:nw)+(is-1)*ni;
0079    x=v_rfft(s(idx).*win);
0080    x2=x.*conj(x);
0081    
0082    pxn=za*pxn+(1-za)*x2;
0083    im=rem(im+1,nm);
0084    if im
0085       mb(:,1)=min(mb(:,1),pxn);
0086    else
0087       mb=[pxn,mb(:,1:po(7)-1)];
0088    end
0089    pn=po(9)*min(mb,[],2);
0090    %os= oversubtraction factor
0091    os=zo*os+(1-zo)*(1+osf.*pn./(pn+pxn));
0092    
0093    px=zg*px+(1-zg)*x2;
0094    q=max(po(10)*sqrt(pn./x2),1-sqrt(os.*pn./px)); 
0095    ss(idx)=ss(idx)+v_irfft(x.*q);
0096    
0097 end
0098 if nargout==0
0099    soundsc([s; ss],fs);
0100 end
0101 
0102 
0103 
0104

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