mlib_signallpcautocorrelgetparcor_s16(3mlib) [sunos man page]
mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB) mediaLib Library Functions mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB) NAME
mlib_SignalLPCAutoCorrelGetPARCOR_S16, mlib_SignalLPCAutoCorrelGetPARCOR_S16_Adp - return the partial correlation (PARCOR) coefficients SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ] #include <mlib.h> mlib_status mlib_SignalLPCAutoCorrelGetPARCOR_S16(mlib_s16 *parcor, mlib_s32 pscale, void *state); mlib_status mlib_SignalLPCAutoCorrelGetPARCOR_S16_Adp(mlib_s16 *parcor, mlib_s32 *pscale, void *state); DESCRIPTION
Each of the functions 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. Note for functions with adaptive scaling (with _Adp postfix), the scaling factor of the output data will be calculated based on the actual data; for functions with non-adaptive scaling (without _Adp postfix), the user supplied scaling factor will be used and the output will be saturated if necessary. PARAMETERS
Each function takes the following arguments: parcor The partial correlation (PARCOR) coefficients. pscale The scaling factor of the partial correlation (PARCOR) coefficients, where actual_data = output_data * 2**(-scaling_fac- tor). state Pointer to the internal state structure. RETURN VALUES
Each 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_S16(3MLIB), mlib_SignalLPCAutoCorrel_S16(3MLIB), mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB), mlib_SignalLP- CAutoCorrelFree_S16(3MLIB), attributes(5) SunOS 5.10 10 Nov 2004 mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB)
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mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB) mediaLib Library Functions mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB) NAME
mlib_SignalLPCAutoCorrelGetPARCOR_S16, mlib_SignalLPCAutoCorrelGetPARCOR_S16_Adp - return the partial correlation (PARCOR) coefficients SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ] #include <mlib.h> mlib_status mlib_SignalLPCAutoCorrelGetPARCOR_S16( mlib_s16 *parcor, mlib_s32 pscale, void *state); mlib_status mlib_SignalLPCAutoCorrelGetPARCOR_S16_Adp( mlib_s16 *parcor, mlib_s32 *pscale, void *state); DESCRIPTION
Each of the functions 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. Note for functions with adaptive scaling (with _Adp postfix), the scaling factor of the output data will be calculated based on the actual data; for functions with non-adaptive scaling (without _Adp postfix), the user supplied scaling factor will be used and the output will be saturated if necessary. PARAMETERS
Each function takes the following arguments: parcor The partial correlation (PARCOR) coefficients. pscale The scaling factor of the partial correlation (PARCOR) coefficients, where actual_data = output_data * 2**(-scaling_factor). state Pointer to the internal state structure. RETURN VALUES
Each 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_SignalLPCAutoCorrelInit_S16(3MLIB), mlib_SignalLPCAutoCorrel_S16(3MLIB), mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB), mlib_SignalLP- CAutoCorrelFree_S16(3MLIB), attributes(5) SunOS 5.11 2 Mar 2007 mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB)