www.pudn.com > voicebox2.zip > DISTCHAR.M

function d=distchar(ar1,ar2,mode) 
%DISTCHAR calculates the cosh spectral 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'. 
% 
% The COSH spectral distance is the average over +ve and -ve frequency of  
% 
%                     cosh(log(p1/p2))-1   =   (p1-p2)^2/(2p1*p2)   =   (p1/p2 + p2/p1)/2 - 1 
% 
% Where p1 and p2 are the power spectra corresponding to the AR coefficient sets AR1 and AR2. 
% The COSH distance is a symmetrical version of the Itakura-Saito distance: distchar(x,y)=(distisar(x,y)+distisar(y,x))/2 
 
% 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 Cosh 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=distchar(ar1,ar2); d1=distchpf(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 
 
 
%      Copyright (C) Mike Brookes 1997 
% 
%      Last modified Thu Jan  6 12:27:07 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); 
p2=p1+1; 
m1=zeros(nf1,2*p1); 
m2=zeros(nf2,2*p1); 
m1(:,1:p1)=lpcar2rr(ar1); 
m1(:,p2:end)=lpcar2ra(ar1); 
m1(:,1)=m1(:,1)*0.5; 
m1(:,p2)=m1(:,p1+1)*0.5; 
m2(:,p2:end)=lpcar2rr(ar2); 
m2(:,1:p1)=lpcar2ra(ar2); 
 
if nargin<3 | isempty(mode) mode='0'; end 
if any(mode=='d') | (mode~='x' & nf1==nf2) 
   nx=min(nf1,nf2); 
   d=sum(m1(1:nx,:).*m2(1:nx,:),2)-1; 
else 
   d=m1*m2'-1; 
end