lpc2par

class diffsptk.LinearPredictiveCoefficientsToParcorCoefficients(lpc_order: int, gamma: float = 1, c: int | None = None)

See this page for details.

Parameters:

lpc_orderint >= 0

The order of the LPC, \(M\).

gammafloat in [-1, 1]

The gamma parameter, \(\gamma\).

cint >= 1 or None

The number of filter stages.

forward(a: Tensor) → Tensor

Convert LPC to PARCOR.

Parameters:

aTensor [shape=(…, M+1)]

The LPC coefficients.

Returns:

outTensor [shape=(…, M+1)]

The PARCOR coefficients.

Examples

>>> import diffsptk
>>> lpc = diffsptk.LPC(5, 2)
>>> lpc2par = diffsptk.LinearPredictiveCoefficientsToParcorCoefficients(2)
>>> x = diffsptk.ramp(1, 5) * 0.1
>>> a = lpc(x)
>>> k = lpc2par(a)
>>> k
tensor([ 0.5054, -0.7273,  0.1193])

diffsptk.functional.lpc2par(a: Tensor, gamma: float = 1, c: int | None = None) → Tensor

Convert LPC to PARCOR.

Parameters:

aTensor [shape=(…, M+1)]

The LPC coefficients.

gammafloat in [-1, 1]

The gamma parameter, \(\gamma\).

cint >= 1 or None

The number of filter stages.

Returns:

outTensor [shape=(…, M+1)]

The PARCOR coefficients.

See also

lpc par2lpc