Content-type: text/html Man page of mlib_SignalLPCCovariance_S16

# mlib_SignalLPCCovariance_S16

Section: mediaLib Library Functions (3MLIB)
Updated: 2 Mar 2007

## 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

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

NAME
SYNOPSIS
DESCRIPTION
PARAMETERS
RETURN VALUES
ATTRIBUTES