ifftr#

class diffsptk.RealValuedInverseFastFourierTransform(fft_length: int, out_length: int | None = None, learnable: bool = False)[source]#

See this page for details.

Parameters:

fft_lengthint >= 2: The FFT length, \(L\).
out_lengthint >= 1 or None: The output length, \(N\).
learnablebool: Whether to make the DFT basis learnable. If True, the module performs DFT rather than FFT.

forward(y: Tensor) → Tensor[source]#

Compute inverse FFT of a complex spectrum.

Parameters:

yTensor [shape=(…, L/2+1)]: The complex input spectrum.

Returns:

outTensor [shape=(…, N)]: The real output signal.

Examples

>>> x = diffsptk.ramp(1, 3)
>>> x
tensor([1., 2., 3.])
>>> fftr = diffsptk.RealValuedFastFourierTransform(8)
>>> ifftr = diffsptk.RealValuedInverseFastFourierTransform(8, 3)
>>> x2 = ifftr(fftr(x))
>>> x2
tensor([1., 2., 3.])

diffsptk.functional.ifftr(y: Tensor, out_length: int | None = None) → Tensor[source]#

Compute inverse FFT of a complex spectrum.

Parameters:

yTensor [shape=(…, L/2+1)]: The complex input spectrum.
out_lengthint or None: The output length, \(N\).

Returns:

outTensor [shape=(…, N)]: The real output signal.