Source code for diffsptk.modules.imglsadf
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import torch
from .base import BaseNonFunctionalModule
from .mglsadf import PseudoMGLSADigitalFilter
[docs]
class PseudoInverseMGLSADigitalFilter(BaseNonFunctionalModule):
"""See :func:`~diffsptk.PseudoMGLSADigitalFilter` for details."""
def __init__(self, filter_order: int, frame_period: int, **kwargs) -> None:
super().__init__()
self.mglsadf = PseudoMGLSADigitalFilter(filter_order, frame_period, **kwargs)
[docs]
def forward(self, y: torch.Tensor, mc: torch.Tensor) -> torch.Tensor:
"""Apply an inverse MGLSA digital filter.
Parameters
----------
y : Tensor [shape=(..., T)]
The input signal.
mc : Tensor [shape=(..., T/P, M+1)] or [shape=(..., T/P, N+M+1)]
The mel-generalized cepstrum, not MLSA digital filter coefficients. Note
that the mixed-phase case assumes that the coefficients are of the form
c_{-N}, ..., c_{0}, ..., c_{M}, where M is the order of the minimum-phase
part and N is the order of the maximum-phase part.
Returns
-------
out : Tensor [shape=(..., T)]
The residual signal.
Examples
--------
>>> M = 4
>>> y = diffsptk.step(3)
>>> mc = diffsptk.nrand(2, M)
>>> mc
tensor([[ 0.8457, 1.5812, 0.1379, 1.6558, 1.4591],
[-1.3714, -0.9669, -1.2025, -1.3683, -0.2352]])
>>> imglsadf = diffsptk.IMLSA(M, frame_period=2)
>>> x = imglsadf(y.view(1, -1), mc.view(1, 2, M + 1))
>>> x
tensor([[ 0.4293, 1.0592, 7.9349, 14.9794]])
"""
x = self.mglsadf(y, -mc)
return x
