lpc2par#

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

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[source]#

Convert LPC to PARCOR.

Parameters:

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

Returns:

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

Examples

>>> x = diffsptk.nrand(4)
>>> x
tensor([ 0.7829, -0.2028,  1.6912,  0.1454,  0.4861])
>>> lpc = diffsptk.LPC(5, 3)
>>> a = lpc(x)
>>> a
tensor([ 1.6036,  0.0573, -0.5615, -0.0638])
>>> lpc2par = diffsptk.LinearPredictiveCoefficientsToParcorCoefficients(3)
>>> k = lpc2par(a)
>>> k
tensor([ 1.6036,  0.0491, -0.5601, -0.0638])

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

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.