autohf.m

FS1016源代码

% 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.
%
% ******************************************************************
% AUTOHF
%
% PORTED TO MATLAB FROM CELP 3.2a C RELEASE
% 6-8-94
%
% ******************************************************************
%
% DESCRIPTION
%
% LPC autocorrelation analysis with high frequency compensation
%
% DESIGN NOTES
%
% Subroutine to perform HF corrected autocorrelation LPC analysis.
% First, autocorrelation coefficients are calculated and high
% frequency corrected to partially compensate for the analog
% antialiasing filter.  (Traditionally, this technique has only been
% applied to covariance analysis, but it applies to autocorrelation
% analysis as well).  Next, the autocorrelation function is converted
% to reflection coefficients by the Schur recursion (aka LeRoux &
% Guegen).  Then, the reflection coefficients are converted to LPC
% predictor coefficients.  Finally, the predictors are bandwidth
% expanded by omega.
%
% LPC predictor coefficient convention is:
%             p+1        -(i-1)
%      A(z) = SUM   a   z          where a  = +1.0
%             i=1    i                    1
%
% The sign convention used defines the first reflection coefficient
% as the normalized first autocorrelation coefficient, which results
% in positive values of rc(1) for voiced speech.
%
% REFERENCES
%
% 1.  Atal & Schroeder, Predictive Coding of Speech Signals
%     and Subjective Error Criteria, IEEE TASSP, June 1979.
%
% VARIABLES
%
% INPUTS
%   si       -      Input sequence (signal)
%   w        -      Window sequence
%   n        -      Input sequence length
%   p        -      LPC predictor polynomial order
%   omega    -      Bandwidth expansion factor for LPC poles
%
% OUTPUTS
%   a        -      LPC coefficients (1 to m+1) (direct form predictor)
%   rc       -      Reflection coefficients (1 to m)
%
% INTERNALS
%   unstable -      Stability flag
%   atemp    -      Predictor coefficient temp storage
%   s        -      Windowed signal sequence
%   c0       -      First autocorrelation lag
%   c        -      Higher order autocorrelation lags
%
% GLOBALS
%   MAXNO    -      LPC predictor order
%   TRUE     -      Flag constant
%   FALSE    -      Flag constant
%   FrameCnt -      Current frame number
%
% ******************************************************************

function [ a, rc ] = autohf( si, w, n, p, omega )

% DECLARE GLOBAL CONSTANTS
global MAXNO TRUE FALSE

% DECLARE GLOBAL VARIABLES
global FrameCnt

% INIT LOCAL VARIABLES
unstable = FALSE;
atemp = zeros( MAXNO+1, 1);
atemp(1) = 1.0;

% APPLY WINDOW TO INPUT SEQUENCE
s = si .* w;

% COMPUTE AUTOCORRELATION
[ c0, c ] = cor( s, n, p );
if c0 < 0.0
    unstable = TRUE;
end

% CONVERT AUTOCORRELATIONS TO PREDICTOR COEFFICIENTS (PCs)
atemp = durbin( c0, c, atemp, p );

% BANDWIDTH EXPAND CORRECTED PCs
a = bwexp( omega, atemp, p );

% MATCH RCs to EXPANDED PCs
rc = pctorc( a, p );

% TEST RCs FOR STABILITY */
if any( abs(rc) > 1.0 )
    unstable = TRUE;
end

% TRAP INSTABILITY EXCEPTIONS
if unstable == TRUE
    printf( 'autohf:  unstable lpc analysis at frame %d\n', FrameCnt );
    a( 2:p+1 ) = 0.00;
    rc( 1:p ) = 0.00;
end