sunos man page for mlib_signallpcautocorrel_s16

mlib_SignalLPCAutoCorrel_S16(3MLIB)			    mediaLib Library Functions			       mlib_SignalLPCAutoCorrel_S16(3MLIB)

NAME
       mlib_SignalLPCAutoCorrel_S16, mlib_SignalLPCAutoCorrel_S16_Adp - perform linear predictive coding with autocorrelation method

SYNOPSIS
       cc [ flag... ] file... -lmlib [ library... ]
       #include <mlib.h>

       mlib_status mlib_SignalLPCAutoCorrel_S16(mlib_s16 *coeff, mlib_s32 cscale, const mlib_s16 *signal, void *state);

       mlib_status mlib_SignalLPCAutoCorrel_S16_Adp(mlib_s16 *coeff, mlib_s32 *cscale, const mlib_s16 *signal, void *state);

DESCRIPTION
       Each function performs linear predictive coding with autocorrelation 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 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

       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 equations can	be  solved
       efficiently with Levinson-Durbin algorithm.

       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	     |Evolving			   |
       +-----------------------------+-----------------------------+
       |MT-Level		     |MT-Safe			   |
       +-----------------------------+-----------------------------+

SEE ALSO
       mlib_SignalLPCAutoCorrelInit_S16(3MLIB),     mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB),    mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB),
       mlib_SignalLPCAutoCorrelFree_S16(3MLIB), attributes(5)

SunOS 5.10							    10 Nov 2004 			       mlib_SignalLPCAutoCorrel_S16(3MLIB)