levdur

class diffsptk.LevinsonDurbin(lpc_order: int, eps: float | None = None, device: device | None = None, dtype: dtype | None = None)

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.

devicetorch.device or None

The device of this module.

dtypetorch.dtype or None

The data type of this module.

forward(r: Tensor) → Tensor

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: Tensor, eps: float | None = None) → Tensor

Solve a Yule-Walker linear system.

Parameters:

rTensor [shape=(…, M+1)]

The autocorrelation.

epsfloat >= 0 or None

A small value to improve numerical stability.

Returns:

outTensor [shape=(…, M+1)]

The gain and LPC coefficients.

See also

acorr lpc rlevdur