poledf#

class diffsptk.AllPoleDigitalFilter(filter_order: int, frame_period: int, ignore_gain: bool = False)[source]#

See this page for details.

Parameters:

filter_orderint >= 0: The order of the filter, \(M\).
frame_periodint >= 1: The frame period in samples, \(P\).
ignore_gainbool: If True, perform filtering without the gain.

References

[1]

C.-Y. Yu et al., “Differentiable time-varying linear prediction in the context of end-to-end analysis-by-synthesis,” Proceedings of Interspeech, 2024.

forward(x: Tensor, a: Tensor) → Tensor[source]#

Apply an all-pole digital filter.

Parameters:

xTensor [shape=(…, T)]: The excitation signal.
aTensor [shape=(…, T/P, M+1)]: The filter coefficients.

Returns:

outTensor [shape=(…, T)]: The output signal.

Examples

>>> x = diffsptk.step(4)
>>> a = diffsptk.ramp(4)
>>> poledf = diffsptk.AllPoleDigitalFilter(0, 1)
>>> y = poledf(x, a.view(-1, 1))
>>> y
tensor([[0., 1., 2., 3., 4.]])

diffsptk.functional.poledf(x: Tensor, a: Tensor, frame_period: int = 80, ignore_gain: bool = False) → Tensor[source]#

Apply an all-pole digital filter.

Parameters:

xTensor [shape=(…, T)]: The excitation signal.
aTensor [shape=(…, T/P, M+1)]: The filter coefficients.
frame_periodint >= 1: The frame period in samples, \(P\).
ignore_gainbool: If True, perform filtering without the gain.

Returns:

outTensor [shape=(…, T)]: The output signal.