mlib_SignalLPCCovariance_S16(3MLIB)			    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.h>

       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 covariance method.

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

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

       where  s(*)  is the speech signal, u(*) is the excitation signal, and G is the gain constants, M is the order of the linear prediction fil-
       ter. 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

       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 decomposition method.

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

       Note for functions with adaptive scaling (with _Adp postfix), the scaling factor of the output data will be calculated 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 coefficients, where actual_data = output_data * 2**(-scaling_factor).

       signal	 The input signal vector with samples in Q15 format.

       state	 Pointer to the internal state structure.

RETURN VALUES

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

ATTRIBUTES

       See attributes(5) for descriptions of the following attributes:

       +-----------------------------+-----------------------------+
       |      ATTRIBUTE TYPE	     |	    ATTRIBUTE VALUE	   |
       +-----------------------------+-----------------------------+
       |Interface Stability	     |Committed 		   |
       +-----------------------------+-----------------------------+
       |MT-Level		     |MT-Safe			   |
       +-----------------------------+-----------------------------+

SEE ALSO

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

SunOS 5.11							    2 Mar 2007				       mlib_SignalLPCCovariance_S16(3MLIB)