05-18-2018
Thanks Bakunin! That helps a lot.
"You seem to not understand how executable code is produced from source code." Have some ideas about it, but not much practical experience.Never tried dissection of the executable code from source code. However, this is not what I want to learn.
That is: which data it expects and what it gives back. That fits exactly what I'm struggling with, as I know the raw data I have at hand and what I expect to get with the raw data.Believe this is all the programs do.
you "need" the header filles and the library itself. Of course you need to know what the functions do, so some text documentation is also necessary. This gives me more information on my question. I need more practice to get my feet wet, and soaked!
Thank you all for the replies!
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LEARN ABOUT SUNOS
mlib_signallpccovariance_f32
mlib_SignalLPCCovariance_F32(3MLIB) mediaLib Library Functions mlib_SignalLPCCovariance_F32(3MLIB)
NAME
mlib_SignalLPCCovariance_F32 - perform linear predictive coding with covariance method
SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ]
#include <mlib.h>
mlib_status mlib_SignalLPCCovariance_F32(mlib_f32 *coeff, const mlib_f32 *signal, void *state);
DESCRIPTION
The mlib_SignalLPCCovariance_F32() 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.
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 |
+-----------------------------+-----------------------------+
SEE ALSO
mlib_SignalLPCCovarianceInit_F32(3MLIB), mlib_SignalLPCCovarianceFree_F32(3MLIB), attributes(5)
SunOS 5.10 10 Nov 2004 mlib_SignalLPCCovariance_F32(3MLIB)