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import torch
from ..typing import Precomputed
from ..utils.private import check_size, filter_values
from .base import BaseFunctionalModule
from .gnorm import GeneralizedCepstrumGainNormalization
[docs]
class GeneralizedCepstrumInverseGainNormalization(BaseFunctionalModule):
"""See `this page <https://sp-nitech.github.io/sptk/latest/main/ignorm.html>`_
for details.
Parameters
----------
cep_order : int >= 0
The order of the cepstrum, :math:`M`.
gamma : float in [-1, 1]
The gamma parameter, :math:`\\gamma`.
c : int >= 1 or None
The number of filter stages.
References
----------
.. [1] T. Kobayashi et al., "Spectral analysis using generalized cepstrum," *IEEE
Transactions on Acoustics, Speech, and Signal Processing*, vol. 32, no. 5,
pp. 1087-1089, 1984.
"""
def __init__(self, cep_order: int, gamma: float = 0, c: int | None = None) -> None:
super().__init__()
self.in_dim = cep_order + 1
self.values = self._precompute(**filter_values(locals()))
[docs]
def forward(self, y: torch.Tensor) -> torch.Tensor:
"""Perform cepstrum inverse gain normalization.
Parameters
----------
y : Tensor [shape=(..., M+1)]
The normalized generalized cepstrum.
Returns
-------
x : Tensor [shape=(..., M+1)]
The generalized cepstrum.
Examples
--------
>>> import diffsptk
>>> gnorm = diffsptk.GeneralizedCepstrumGainNormalization(3, 0.5)
>>> ignorm = diffsptk.GeneralizedCepstrumInverseGainNormalization(3, 0.5)
>>> x = diffsptk.ramp(1, 4)
>>> x2 = ignorm(gnorm(x))
>>> x2
tensor([1., 2., 3., 4.])
"""
check_size(y.size(-1), self.in_dim, "dimension of cepstrum")
return self._forward(y, *self.values)
@staticmethod
def _func(y: torch.Tensor, *args, **kwargs) -> torch.Tensor:
values = GeneralizedCepstrumInverseGainNormalization._precompute(
y.size(-1) - 1, *args, **kwargs
)
return GeneralizedCepstrumInverseGainNormalization._forward(y, *values)
@staticmethod
def _takes_input_size() -> bool:
return True
@staticmethod
def _check(*args, **kwargs) -> None:
raise NotImplementedError
@staticmethod
def _precompute(*args, **kwargs) -> Precomputed:
return GeneralizedCepstrumGainNormalization._precompute(*args, **kwargs)
@staticmethod
def _forward(y: torch.Tensor, gamma: float) -> torch.Tensor:
K, y = torch.split(y, [1, y.size(-1) - 1], dim=-1)
if gamma == 0:
x0 = torch.log(K)
x1 = y
else:
z = torch.pow(K, gamma)
x0 = (z - 1) / gamma
x1 = y * z
x = torch.cat((x0, x1), dim=-1)
return x
