frame#

class diffsptk.Frame(frame_length: int, frame_period: int, *, center: bool = True, zmean: bool = False, mode: str = 'constant')[source]#

See this page for details.

Parameters:

frame_lengthint >= 1: The frame length in samples, \(L\).
frame_periodint >= 1: The frame period in samples, \(P\).
centerbool: If True, pad the input on both sides so that the frame is centered.
zmeanbool: If True, perform mean subtraction on each frame.
mode[‘constant’, ‘reflect’, ‘replicate’, ‘circular’]: The padding method.

forward(x: Tensor) → Tensor[source]#

Apply framing to the given waveform.

Parameters:

xTensor [shape=(…, T)]: The waveform.

Returns:

outTensor [shape=(…, T/P, L)]: The framed waveform.

Examples

>>> x = diffsptk.ramp(1, 9)
>>> frame = diffsptk.Frame(5, 2)
>>> y = frame(x)
>>> y
tensor([[0., 0., 1., 2., 3.],
        [1., 2., 3., 4., 5.],
        [3., 4., 5., 6., 7.],
        [5., 6., 7., 8., 9.],
        [7., 8., 9., 0., 0.]])

diffsptk.functional.frame(x: Tensor, frame_length: int = 400, frame_period: int = 80, center: bool = True, zmean: bool = False, mode: str = 'constant') → Tensor[source]#

Apply framing to the given waveform.

Parameters:

xTensor [shape=(…, T)]: The waveform.
frame_lengthint >= 1: The frame length in samples, \(L\).
frame_periodint >= 1: The frame period in samples, \(P\).
centerbool: If True, pad the input on both sides so that the frame is centered.
zmeanbool: If True, perform mean subtraction on each frame.
mode[‘constant’, ‘reflect’, ‘replicate’, ‘circular’]: The padding method.

Returns:

outTensor [shape=(…, T/P, L)]: The framed waveform.