tf.signal.dct

Compat aliases for migration

See Migration guide for more details.

tf.signal.dct(
    input, type=2, n=None, axis=-1, norm=None, name=None
)

Currently only Types I, II and III are supported. Type I is implemented using a length 2N padded tf.signal.rfft. Type II is implemented using a length 2N padded tf.signal.rfft, as described here: Type 2 DCT using 2N FFT padded (Makhoul). Type III is a fairly straightforward inverse of Type II (i.e. using a length 2N padded tf.signal.irfft).

Args
`input`	A `[..., samples]` `float32`/`float64` `Tensor` containing the signals to take the DCT of.
`type`	The DCT type to perform. Must be 1, 2 or 3.
`n`	The length of the transform. If length is less than sequence length, only the first n elements of the sequence are considered for the DCT. If n is greater than the sequence length, zeros are padded and then the DCT is computed as usual.
`axis`	For future expansion. The axis to compute the DCT along. Must be `-1`.
`norm`	The normalization to apply. `None` for no normalization or `'ortho'` for orthonormal normalization.
`name`	An optional name for the operation.

Returns
A `[..., samples]` `float32`/`float64` `Tensor` containing the DCT of `input`.

Raises
`ValueError`	If `type` is not `1`, `2` or `3`, `axis` is not `-1`, `n` is not `None` or greater than 0, or `norm` is not `None` or `'ortho'`.
`ValueError`	If `type` is `1` and `norm` is `ortho`.

Equivalent to scipy.fftpack.dct for Type-I, Type-II and Type-III DCT.

Args