tf.signal.mdct
Stay organized with collections Save and categorize content based on your preferences.

Compat aliases for migration

tf.compat.v1.signal.mdct

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`.

tf.signal.mdctStay organized with collections Save and categorize content based on your preferences.