Programmer Guide/Command Reference/EVAL/lpc: Difference between revisions
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Compute the linear prediction coefficients using the autocorrelation method | Compute the linear prediction coefficients using the autocorrelation method. This function implements an all-pole filter approximation of the acoustic tube modell of the vocal tract. The implementation is based on the book "Linear Prediction of Speech" (J.D. Markel & A.H. Gray, Springer 1976). | ||
;Usage:<code>lpc(<var>x</var>, <var>m</var>, <var>p</var> {, <var>type</var> {, <var>lfft</var>}})</code> | ;Usage:<code>lpc(<var>x</var>, <var>m</var>, <var>p</var> {, <var>type</var> {, <var>lfft</var>}})</code> | ||
:;<var>x</var>the signal vector; this should be a speech signal without windowing function (because the hamming-window is applied to ''x'' by this the function) | :;<var>x</var>the signal vector; this should be a speech signal without windowing function (because the hamming-window is applied to ''x'' by this the function) | ||
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:# use the autocorelation method to compute the inverse filter coefficients ''ai'', the reflection coefficients ''rc'' and the error (or residual) energy ''alpha'' | :# use the autocorelation method to compute the inverse filter coefficients ''ai'', the reflection coefficients ''rc'' and the error (or residual) energy ''alpha'' | ||
:# convert coefficients to the selected result | :# convert coefficients to the selected result | ||
;Result:A vector ''y'' containing the result of the function. | ;Result:A vector ''y'' containing the result of the function. | ||
:{|class="einrahmen" | :{|class="einrahmen" | ||
Revision as of 14:19, 12 April 2011
Compute the linear prediction coefficients using the autocorrelation method. This function implements an all-pole filter approximation of the acoustic tube modell of the vocal tract. The implementation is based on the book "Linear Prediction of Speech" (J.D. Markel & A.H. Gray, Springer 1976).
- Usage
lpc(x, m, p {, type {, lfft}})- xthe signal vector; this should be a speech signal without windowing function (because the hamming-window is applied to x by this the function)
- m
- number of coefficients
- rule of thumb: m ~ samplingrate / 1000 * 1.25
- pdifferentiation factor; 0 <= p <= 1 (default=0)
- type
- output selector; 0 <= type <= 4 (default=0)
- lfft
- the length of the fft to be used for the computation of the transfer function (amplitude spectrum) of the inverse filter; m+1 < lfft
- Description
-
- apply differentiation to signal x
- apply hamming window to signal x
- use the autocorelation method to compute the inverse filter coefficients ai, the reflection coefficients rc and the error (or residual) energy alpha
- convert coefficients to the selected result
- Result
- A vector y containing the result of the function.
type y nrow(y) description 0 amplitude spectrum of the inverse filter lfft/2+1 this function can be used in speech analysis to compute the transfer function of the vocal tract (e.g. for formant extraction) 1 y[0]=alphay[1..M+1]=ai[0..m]m+2 the error energy (alpha) and the m+1 inverse filter coefficients ai 2 y[0]=alphay[1..M]=rc[0..m-1]m+1 the error energy (alpha) and the m reflection coefficients rc 3 y[0]=alphay[1..M+1]=ar[0..m]m+2 the error energy (alpha) and the m area coefficients ar
(ar[i] ~ area of section i)4 y[0]=alphay[1..M+1]=lar[0..m]m+2 the error energy (alpha) and the m log. area coefficients lar
(lar[i] ~ diameter of section i)
