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tf.signal.mdct

View source on GitHub

Computes the Modified Discrete Cosine Transform of signals.

tf.signal.mdct(
    signals, frame_length, window_fn=tf.signal.vorbis_window, pad_end=False,
    norm=None, name=None
)

Implemented with TPU/GPU-compatible ops and supports gradients.

Args:

  • signals: A [..., samples] float32/float64 Tensor of real-valued signals.
  • frame_length: An integer scalar Tensor. The window length in samples which must be divisible by 4.
  • window_fn: A callable that takes a frame_length and a dtype keyword argument and returns a [frame_length] Tensor of samples in the provided datatype. If set to None, a rectangular window with a scale of 1/sqrt(2) is used. For perfect reconstruction of a signal from mdct followed by inverse_mdct, please use tf.signal.vorbis_window, tf.signal.kaiser_bessel_derived_window or None. If using another window function, make sure that w[n]^2 + w[n + frame_length // 2]^2 = 1 and w[n] = w[frame_length - n - 1] for n = 0,...,frame_length // 2 - 1 to achieve perfect reconstruction.
  • pad_end: Whether to pad the end of signals with zeros when the provided frame length and step produces a frame that lies partially past its end.
  • norm: If it is None, unnormalized dct4 is used, if it is "ortho" orthonormal dct4 is used.
  • name: An optional name for the operation.

Returns:

A [..., frames, frame_length // 2] Tensor of float32/float64 MDCT values where frames is roughly samples // (frame_length // 2) when pad_end=False.

Raises:

  • ValueError: If signals is not at least rank 1, frame_length is not scalar, or frame_length is not a multiple of 4.