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 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

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)