Laura Thomson on coding, the workplace, and FOSS


 
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Old 05-13-2008
Laura Thomson on coding, the workplace, and FOSS

Tue, 13 May 2008 20:00:00 GMT
Ever since Laura Thomson wrote her first program in the fourth grade, coding has been a major part of her life. Over the years, she has been a lecturer in computer science at RMIT University in Australia, a principal at OmnTI, a consulting company that designs Internet systems, a trainer of other programmers, the co-writer of PHP and MySQL Web Development and MySQL Tutorial, and a frequent speaker at free and open source conferences. She is currently a senior software engineer at the Mozilla Corporation, where her recent work includes the API for the Add-ons Manager on Firefox 3. With this background, Thomson has strong views on coding, its future, and its place in business, especially where free and open source software (FOSS) is concerned, which she shared with Linux.com at the recent Open Web Vancouver conference.


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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 |Committed | +-----------------------------+-----------------------------+ |MT-Level |MT-Safe | +-----------------------------+-----------------------------+ SEE ALSO
mlib_SignalLPCCovarianceInit_F32(3MLIB), mlib_SignalLPCCovarianceFree_F32(3MLIB), attributes(5) SunOS 5.11 2 Mar 2007 mlib_SignalLPCCovariance_F32(3MLIB)