# mlib_signallpcautocorrel_f32(3mlib) [sunos man page]

```mlib_SignalLPCAutoCorrel_F32(3MLIB)			    mediaLib Library Functions			       mlib_SignalLPCAutoCorrel_F32(3MLIB)

NAME
mlib_SignalLPCAutoCorrel_F32 - perform linear predictive coding with autocorrelation method

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

mlib_status mlib_SignalLPCAutoCorrel_F32(mlib_f32 *coeff, const mlib_f32 *signal, void *state);

DESCRIPTION
The mlib_SignalLPCAutoCorrel_F32() 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.

PARAMETERS
The function takes the following arguments:

coeff	       The linear prediction coefficients.

signal	       The input signal vector.

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			   |
+-----------------------------+-----------------------------+

mlib_SignalLPCAutoCorrelInit_F32(3MLIB),     mlib_SignalLPCAutoCorrelGetEnergy_F32(3MLIB),    mlib_SignalLPCAutoCorrelGetPARCOR_F32(3MLIB),
mlib_SignalLPCAutoCorrelFree_F32(3MLIB), attributes(5)

SunOS 5.10							    10 Nov 2004 			       mlib_SignalLPCAutoCorrel_F32(3MLIB)```

## Check Out this Related Man Page

```mlib_SignalLPCAutoCorrelGetPARCOR_F32(3MLIB)		    mediaLib Library Functions		      mlib_SignalLPCAutoCorrelGetPARCOR_F32(3MLIB)

NAME
mlib_SignalLPCAutoCorrelGetPARCOR_F32 - return the partial correlation (PARCOR) coefficients

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

mlib_status mlib_SignalLPCAutoCorrelGetPARCOR_F32(mlib_f32 *parcor, void *state);

DESCRIPTION
The mlib_SignalLPCAutoCorrelGetPARCOR_F32() function returns the partial correlation (PARCOR) coefficients.

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.

PARAMETERS
The function takes the following arguments:

parcor	       The partial correlation (PARCOR) coefficients.

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			   |
+-----------------------------+-----------------------------+