levdur#

class diffsptk.LevinsonDurbin(lpc_order, eps=0)[source]#

See this page for details. The implementation is based on a simple matrix inversion.

Parameters:

lpc_orderint >= 0: The order of the LPC coefficients, \(M\).
epsfloat >= 0: A small value to improve numerical stability.

forward(r)[source]#

Solve a Yule-Walker linear system.

Parameters:

rTensor [shape=(…, M+1)]: The autocorrelation.

Returns:

outTensor [shape=(…, M+1)]: The gain and the LPC coefficients.

Examples

>>> x = diffsptk.nrand(4)
>>> x
tensor([ 0.8226, -0.0284, -0.5715,  0.2127,  0.1217])
>>> acorr = diffsptk.Autocorrelation(5, 2)
>>> levdur = diffsptk.LevinsonDurbin(2)
>>> a = levdur(acorr(x))
>>> a
tensor([0.8726, 0.1475, 0.5270])

diffsptk.functional.levdur(r, eps=0)[source]#

Solve a Yule-Walker linear system.

Parameters:

rTensor [shape=(…, M+1)]: The autocorrelation.
epsfloat >= 0: A small value to improve numerical stability.

Returns:

outTensor [shape=(…, M+1)]: The gain and LPC coefficients.

levdur#

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