Source code for diffsptk.modules.zcross

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# Copyright 2022 SPTK Working Group                                        #
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import torch
import torch.nn as nn

from ..misc.utils import replicate1




class ZeroCrossingAnalysis(nn.Module):
    """See `this page <https://sp-nitech.github.io/sptk/latest/main/zcross.html>`_
    for details. **Note that this module cannot compute gradient**.

    Parameters
    ----------
    frame_length : int >= 1
        Frame length, :math:`L`.

    norm : bool
        If True, divide zero-crossing rate by frame length.

    """

    def __init__(self, frame_length, norm=False):
        super(ZeroCrossingAnalysis, self).__init__()

        assert 1 <= frame_length

        self.frame_length = frame_length
        self.norm = norm



    def forward(self, x):
        """Compute zero-crossing rate.

        Parameters
        ----------
        x : Tensor [shape=(..., T)]
            Waveform.

        Returns
        -------
        out : Tensor [shape=(..., T/L)]
            Zero-crossing rate.

        Examples
        --------
        >>> x = diffsptk.nrand(5)
        >>> x
        tensor([-0.2388,  0.3587, -0.6606, -0.6929,  0.5239,  0.4501])
        >>> zcross = diffsptk.ZeroCrossingAnalysis(3)
        >>> z = zcross(x)
        >>> z
        tensor([2., 1.])

        """
        return self._forward(x, self.frame_length, self.norm)


    @staticmethod
    def _forward(x, frame_length, norm):
        x = torch.sign(x)
        x = replicate1(x, right=False)
        x = x.unfold(-1, frame_length + 1, frame_length)
        z = 0.5 * (x[..., 1:] - x[..., :-1]).abs().sum(-1)
        if norm:
            z /= frame_length
        return z

    _func = _forward