hilbert#

class diffsptk.HilbertTransform(fft_length: int, dim: int = -1, device: device | None = None, dtype: dtype | None = None)[source]#

Hilbert transform module.

Parameters:

fft_lengthint >= 1: The number of FFT bins.
dimint: The dimension along which to take the Hilbert transform.
devicetorch.device or None: The device of this module.
dtypetorch.dtype or None: The data type of this module.

forward(x: Tensor) → Tensor[source]#

Compute the analytic signal using the Hilbert transform.

Parameters:

xTensor [shape=(…, T, …)]: The input signal.

Returns:

outTensor [shape=(…, T, …)]: The analytic signal, where the real part is the input signal and the imaginary part is the Hilbert transform of the input signal.

Examples

>>> import diffsptk
>>> hilbert = diffsptk.HilbertTransform(4)
>>> x = diffsptk.ramp(3)
>>> z = hilbert(x)
>>> z.real
tensor([0., 1., 2., 3.])
>>> z.imag
tensor([ 1., -1., -1.,  1.])

diffsptk.functional.hilbert(x: Tensor, fft_length: int | None = None, dim: int = -1) → Tensor[source]#

Compute the analytic signal using the Hilbert transform.

Parameters:

xTensor [shape=(…, T, …)]: The input signal.
fft_lengthint >= 1 or None: The number of FFT bins. If None, set to \(T\).
dimint: The dimension along which to take the Hilbert transform.

Returns:

outTensor [shape=(…, T, …)]: The analytic signal.