mediaLib Library Functions
                     mlibSignalLPCAutoCorrelGetPARCORS16(3MLIB)



NAME
     mlibSignalLPCAutoCorrelGetPARCORS16,
     mlibSignalLPCAutoCorrelGetPARCORS16Adp  - return the par-
     tial correlation (PARCOR) coefficients

SYNOPSIS
     cc [ flag... ] file... -lmlib [ library... ]
     #include 

     mlibstatus mlibSignalLPCAutoCorrelGetPARCORS16(
          mlibs16 *parcor, mlibs32 pscale, void *state);


     mlibstatus mlibSignalLPCAutoCorrelGetPARCORS16Adp(
          mlibs16 *parcor, mlibs32 *pscale, void *state);


DESCRIPTION
     Each of the functions returns the partial correlation  (PAR-
     COR) coefficients.


     In linear predictive coding (LPC) model, each speech  sample
     is  represented  as  a linear combination of the past M sam-
     ples.

                    M
            s(n) = SUM a(i) * s(n-i) ] G * u(n)
                   i=1



     where s(*) is the speech signal, u(*) is the excitation sig-
     nal,  and  G  is  the  gain constants, M is the order of the
     linear prediction filter. Given s(*), the goal is to find  a
     set  of coefficient a(*) that minimizes the prediction error
     e(*).

                           M
            e(n) = s(n) - SUM a(i) * s(n-i)
                          i=1



     In autocorrelation method, the coefficients can be  obtained
     by solving following set of linear equations.

             M
            SUM a(i) * r(i-k) = r(k), k=1,...,M
            i=1




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mediaLib Library Functions
                     mlibSignalLPCAutoCorrelGetPARCORS16(3MLIB)



     where

                  N-k-1
            r(k) = SUM s(j) * s(j]k)
                   j=0



     are the autocorrelation  coefficients  of  s(*),  N  is  the
     length of the input speech vector. r(0) is the energy of the
     speech signal.


     Note that the autocorrelation matrix R is a Toeplitz  matrix
     (symmetric  with all diagonal elements equal), and the equa-
     tions can be solved efficiently with  Levinson-Durbin  algo-
     rithm.


     See Fundamentals of Speech Recognition by  Lawrence  Rabiner
     and Biing-Hwang Juang, Prentice Hall, 1993.


     Note for functions with adaptive scaling  (with  Adp  post-
     fix),  the  scaling factor of the output data will be calcu-
     lated based on the actual  data;  for  functions  with  non-
     adaptive  scaling  (without Adp postfix), the user supplied
     scaling factor will be used and the output will be saturated
     if necessary.

PARAMETERS
     Each function takes the following arguments:

     parcor    The partial correlation (PARCOR) coefficients.


     pscale    The scaling  factor  of  the  partial  correlation
               (PARCOR)   coefficients,   where   actualdata   =
               outputdata * 2**(-scalingfactor).


     state     Pointer to the internal state structure.


RETURN VALUES
     Each function returns MLIBSUCES if successful.  Otherwise
     it returns MLIBFAILURE.

ATRIBUTES
     See attributes(5) for descriptions of the  following  attri-
     butes:



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mediaLib Library Functions
                     mlibSignalLPCAutoCorrelGetPARCORS16(3MLIB)



     
           ATRIBUTE TYPE               ATRIBUTE VALUE       
    
     Interface Stability          Committed                   
    
     MT-Level                     MT-Safe                     
    


SEE ALSO
     mlibSignalLPCAutoCorrelInitS16(3MLIB),
     mlibSignalLPCAutoCorrelS16(3MLIB),
     mlibSignalLPCAutoCorrelGetEnergyS16(3MLIB),
     mlibSignalLPCAutoCorrelFreeS16(3MLIB), attributes(5)








































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mediaLib Library Functions
                     mlibSignalLPCAutoCorrelGetPARCORS16(3MLIB)






















































SunOS 5.11           Last change: 2 Mar 2007                    4