rctoac.m

实现fs1016w的CELP的低速率语音编解码功能的基于vc开发环境的原代码。

% MATLAB SIMULATION OF NSA FS-1016 CELP v3.2
% COPYRIGHT (C) 1995-99 ANDREAS SPANIAS AND TED PAINTER
%
% This Copyright applies only to this particular MATLAB implementation
% of the FS-1016 CELP coder.  The MATLAB software is intended only for educational
% purposes.  No other use is intended or authorized.  This is not a public
% domain program and distribution to individuals or networks is strictly
% prohibited.  Be aware that use of the standard in any form is goverened
% by rules of the US DoD.  Therefore patents and royalties may apply to
% authors, companies, or committees associated with this standard, FS-1016.  For
% questions regarding the MATLAB implementation please contact Andreas
% Spanias at [phone].  For questions on rules,
% royalties, or patents associated with the standard, please contact the DoD.
%
% ALL DERIVATIVE WORKS MUST INCLUDE THIS COPYRIGHT NOTICE.
%
% ******************************************************************
% RCTOAC
%
% PORTED TO MATLAB FROM CELP 3.2a C RELEASE
% 6-16-94
%
% ******************************************************************
%
% DESCRIPTION
%
% Convert reflection coefficients to autocorrelation coefficients
%
% DESIGN NOTES
%
% Sign convention is:
%
% First reflection coefficient = +(normalized autocorrelation coefficient)
%
% REFERENCES
%
% 1. Atal & Hanauer, "Speech Analysis and Synthesis by Linear
%    Prediction of the Speech Wave," JASA, Vol 50 (2), 1971.
%
% VARIABLES
%
% INPUTS
%   rc         -     Reflection coefficients
%   m          -     Predictor order
%
% OUTPUTS
%   r          -     Normalized autocorrelation lags
%
% INTERNALS
%   t          -     Predictor polynomial
%   z          -     Upper limit for vector multiply operations
%   tj         -     Intermediate results of the recursion
%   tkj        -     "                "              "
%   k          -     Loop counter
%
% ******************************************************************

function r = rctoac( rc, m )

% INITIALIZE LOCAL VARIABLES
r = zeros( m+1, 1 );
t = r;
r(1) = 1.0;
r(2:m+1) = rc;

% COMPUTE PREDICTOR POLYNOMIAL OF DIFFERENT DEGREE AND STORE IN T
% COMPUTE AUTOCORRELATION AND STORE IN R
t(1) = 1.0;
t(2) = -r(2);
if m > 1
    for k = 2:m
        z = fix(k/2);
        tj = t( 2:z+1 ) - ( r( k+1 ) * t( k:-1:k-z+1 ) );
        tkj = t( k:-1:k-z+1 ) - ( r( k+1 ) * t( 2:z+1 ) );
        t( 2:z+1 ) = tj;
        t( k:-1:k-z+1 ) = tkj;
        t( k+1 ) = -r( k+1 );
        r( k+1 ) = r( k+1 ) - sum( t( 2:k ) .* r( k:-1:2 ) );
    end
end