mlib_signallpcperceptweight_s16(3mlib) [sunos man page]

mlib_SignalLPCPerceptWeight_S16(3MLIB)			    mediaLib Library Functions			    mlib_SignalLPCPerceptWeight_S16(3MLIB)

NAME

       mlib_SignalLPCPerceptWeight_S16 - perform perceptual weighting on input signal

SYNOPSIS

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

       mlib_status  mlib_SignalLPCPerceptWeight_S16(mlib_s16  *sigwgt,	const mlib_s16 *signal, const mlib_s16 *lpc, mlib_s32 lscale, mlib_s16 r1,
       mlib_s16 r2, void *state);

DESCRIPTION

       The mlib_SignalLPCPerceptWeight_S16() function performs perceptual weighting on input signal.

       The perceptual weighting filter is defined as following.

		    A(z*r1)
	    W(z) = ---------
		    A(z*r2)

       where A(z) is the inverse filter

			M	   -i
	    A(z) = 1 - SUM a(i) * z
		       i=1

       See G.723.1, G.728, G.729, G.729A, GSM EFR standards.

PARAMETERS

       The function takes the following arguments:

       sigwgt	       The weighted signal vector, the signal samples are in Q15 format.

       signal	       The input signal vector, the signal samples are in Q15 format.

       lpc	       The linear prediction coefficients.

       lscale	       The scaling factor of the linear prediction coefficients, where actual_data = input_data * 2**(-scaling_factor).

       r1	       The perceptual weighting filter coefficient, the coefficient is in Q15 format, it is treated as 1 if 0 is supplied.

       r2	       The perceptual weighting filter coefficient, the coefficient is in Q15 format, it is treated as 1 if 0 is supplied.

       state	       Pointer to the internal state structure.

RETURN VALUES

       The 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_SignalLPCPerceptWeightInit_S16(3MLIB), mlib_SignalLPCPerceptWeightFree_S16(3MLIB), attributes(5)

SunOS 5.10							    10 Nov 2004 			    mlib_SignalLPCPerceptWeight_S16(3MLIB)

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

SEE ALSO

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

SunOS 5.10							    10 Nov 2004 			       mlib_SignalLPCCovariance_S16(3MLIB)