Armadillo C++ Library 0.4.0 (Default branch)


 
Thread Tools Search this Thread
Special Forums News, Links, Events and Announcements Software Releases - RSS News Armadillo C++ Library 0.4.0 (Default branch)
# 1  
Old 01-28-2009
Armadillo C++ Library 0.4.0 (Default branch)

Armadillo is a C++ linear algebra library (matrix and vector maths) aiming towards a good balance between speed and ease of use. Its intended target audience is scientists and engineers. A delayed evaluation approach, based on template meta-programming, is used (during compile time) to combine several operations into one and reduce or eliminate the need for temporaries. Where applicable, the order of operations is optimized. Furthermore, optional interfaces to LAPACK and ATLAS functions are provided.License: GNU General Public License v2Changes:
Configuration and installation are considerablyeasier. Many functions for statistics were added.Handling of complex numbers was improved.Image

Image

More...
Login or Register to Ask a Question

Previous Thread | Next Thread
Login or Register to Ask a Question
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 | +-----------------------------+-----------------------------+ SEE ALSO
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)