Armadillo C++ Library 0.3.0 (Default branch)


 
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Old 01-16-2009
Armadillo C++ Library 0.3.0 (Default branch)

The Armadillo C++ Library aims to provide anefficient base for linear algebra operations(matrix and vector maths) while having astraightforward and easy to use interface. Itsintended target audience is scientists andengineers. A delayed evaluation approach isemployed (during compile time) in order to combineseveral operations into one and reduce oreliminate the need for temporaries. Whereapplicable, the order of operations is optimized.Delayed evaluation and optimization are achievedthrough recursive templates and templatemeta-programming.License: GNU General Public License v2Changes:
Support was added for complex numbers as well asother types (e.g. int, float). Trigonometricfunctions were added.Image

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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)