Manual Pages for UNIX Operating System command usage for man mlib_SignalLPCCovariance_S16

mediaLib Library Functions    mlib_SignalLPCCovariance_S16(3MLIB)



NAME
     mlib_SignalLPCCovariance_S16,
     mlib_SignalLPCCovariance_S16_Adp - perform linear predictive
     coding with covariance method

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

     mlib_status mlib_SignalLPCCovariance_S16(mlib_s16 *coeff,
          mlib_s32 cscale, const mlib_s16 *signal, void *state);


     mlib_status mlib_SignalLPCCovariance_S16_Adp(mlib_s16 *coeff,
          mlib_s32 *cscale, const mlib_s16 *signal, void *state);


DESCRIPTION
     Each function performs linear predictive coding with covari-
     ance method.


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

             M
            SUM a(i) * c(i,k) = c(0,k), k=1,...,M
            i=1





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mediaLib Library Functions    mlib_SignalLPCCovariance_S16(3MLIB)



     where

                     N-k-1
            c(i,k) =  SUM s(j) * s(j+k-i)
                      j=0



     are the covariance coefficients of s(*), N is the length  of
     the input speech vector.


     Note that the covariance matrix R is a symmetric matrix, and
     the equations can be solved efficiently with Cholesky decom-
     position method.


     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:

     coeff     The linear prediction coefficients.


     cscale    The scaling factor of the linear prediction  coef-
               ficients,  where actual_data = output_data * 2**(-
               scaling_factor).


     signal    The input signal vector with samples in  Q15  for-
               mat.


     state     Pointer to the internal state structure.


RETURN VALUES
     Each function returns MLIB_SUCCESS if successful.  Otherwise
     it returns MLIB_FAILURE.

ATTRIBUTES




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mediaLib Library Functions    mlib_SignalLPCCovariance_S16(3MLIB)



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



     ____________________________________________________________
    |       ATTRIBUTE TYPE        |       ATTRIBUTE VALUE       |
    |_____________________________|_____________________________|
    | Interface Stability         | Committed                   |
    |_____________________________|_____________________________|
    | MT-Level                    | MT-Safe                     |
    |_____________________________|_____________________________|


SEE ALSO
     mlib_SignalLPCCovarianceInit_S16(3MLIB),
     mlib_SignalLPCCovarianceFree_S16(3MLIB), attributes(5)






































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