Programmer Guide/SPU Reference/LPCOEFS: Difference between revisions

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This function computes the LP (linear prediction) coefficients for the speech signal x using the autocorrelation method. The signal vector <var>x</var> must be windowed before being connected to this atom. An optional pre-filtering (differentiation, if <var>d</var>><code>0</code>) is applied to the signal before the coefficients' iteration. The algorithms used in this function are based on the algorithms published by J.D.Markel and A.H.Gray in "Linear Prediction of Speech".
This function computes the LP (linear prediction) coefficients for the speech signal x using the autocorrelation method. The signal vector <var>x</var> must be windowed before being connected to this atom. An optional pre-filtering (differentiation, if <var>d</var>><code>0</code>) is applied to the signal before the coefficients' iteration. The algorithms used in this function are based on the algorithms published by J.D.Markel and A.H.Gray in "Linear Prediction of Speech".


Linear prediction coefficients selected by input <var>type</var>{|
Linear prediction coefficients selected by input <var>type</var>
{|
|-
|-
|value of <var>type</var>
|value of <var>type</var>

Latest revision as of 11:50, 3 May 2018

LPCOEFS - linear prediction coefs.

Usage:

LPCOEFS x n d type

Inputs:
x signal vector no default
n number of coefficients def=20
d pre-emphasis (differentiation factor, 0..1) def.=1
type selects the coefficient type def.=RC
Outputs:
y coefficients (n or n+1 values)
alpha error energy
rc reflection coefficients
Function:

This function computes the LP (linear prediction) coefficients for the speech signal x using the autocorrelation method. The signal vector x must be windowed before being connected to this atom. An optional pre-filtering (differentiation, if d>0) is applied to the signal before the coefficients' iteration. The algorithms used in this function are based on the algorithms published by J.D.Markel and A.H.Gray in "Linear Prediction of Speech".

Linear prediction coefficients selected by input type

value of type type of coefficients stored in y number of coefs.
0 or AI inverse filter coefficients n+1
1 or RC reflection coefficients n
2 or AREA area coefficients n
3 or LOGAREA logarithmic area coefficients n

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