www.gusucode.com > 语音信号处理工具箱 - Voicebox源码程序 > Voicebox\distitar.m
function d=distitar(ar1,ar2,mode) %DISTITAR calculates the Itakura distance between AR coefficients D=(AR1,AR2,MODE) % % Inputs: AR1,AR2 AR coefficient sets to be compared. Each row contains a set of coefficients. % AR1 and AR2 must have the same number of columns. % % MODE Character string selecting the following options: % 'x' Calculate the full distance matrix from every row of AR1 to every row of AR2 % 'd' Calculate only the distance between corresponding rows of AR1 and AR2 % The default is 'd' if AR1 and AR2 have the same number of rows otherwise 'x'. % % Output: D If MODE='d' then D is a column vector with the same number of rows as the shorter of AR1 and AR2. % If MODE='x' then D is a matrix with the same number of rows as AR1 and the same number of columns as AR2'. % % If ave() denotes the average over +ve and -ve frequency, the Itakura spectral distance is % % log(ave(pf1/pf2)) - ave(log(pf1/pf2)) % % The Itakura distance is gain-independent, i.e. distitpf(f*pf1,g*pf2) is independent of f and g. % % The Itakura distance may be expressed as log(ar2*toeplitz(lpcar2rr(ar1))*ar2') where the ar1 and ar2 polynomials % have first been normalised by dividing through by their 0'th order coefficients. % Since the power spectrum is the fourier transform of the autocorrelation, we can calculate % the average value of p1/p2 by taking the 0'th order term of the convolution of the autocorrelation % functions associated with p1 and 1/p2. Since 1/p2 corresponds to an FIR filter, this convolution is % a finite sum even though the autocorrelation function of p1 is infinite in extent. % The average value of log(pf1) is equal to log(ar1(1)^-2) where ar1(1) is the 0'th order AR coefficient. % The Itakura distance can also be calculated directly from the power spectra; providing np is large % enough, the values of d0 and d1 in the following will be very similar: % % np=255; d0=distitar(ar1,ar2); d1=distitpf(lpcar2pf(ar1,np),lpcar2pf(ar2,np)) % % Ref: A.H.Gray Jr and J.D.Markel, "Distance measures for speech processing", IEEE ASSP-24(5): 380-391, Oct 1976 % L. Rabiner abd B-H Juang, "Fundamentals of Speech Recognition", Section 4.5, Prentice-Hall 1993, ISBN 0-13-015157-2 % F. Itakura, "Minimum prediction residual principle applied to speech recognition", IEEE ASSP-23: 62-72, 1975 % Copyright (C) Mike Brookes 1997 % % Last modified Fri Jan 7 08:59:56 2000 % % VOICEBOX is a MATLAB toolbox for speech processing. Home page is at % http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation; either version 2 of the License, or % (at your option) any later version. % % This program is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You can obtain a copy of the GNU General Public License from % ftp://prep.ai.mit.edu/pub/gnu/COPYING-2.0 or by writing to % Free Software Foundation, Inc.,675 Mass Ave, Cambridge, MA 02139, USA. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% [nf1,p1]=size(ar1); nf2=size(ar2,1); m2=lpcar2ra(ar2); m2(:,1)=0.5*m2(:,1); if nargin<3 | isempty(mode) mode='0'; end if any(mode=='d') | (mode~='x' & nf1==nf2) nx=min(nf1,nf2); d=log(2*sum(lpcar2rr(ar1(1:nx,:)).*m2(1:nx,:),2).*((ar1(1:nx,1)./ar2(1:nx,1)).^2)); else d=log(2*lpcar2rr(ar1)*m2'.*((ar1(:,1)*ar2(:,1)'.^(-1)).^2)); end