signals#

diffsptk.impulse(order, **kwargs)[source]#

Generate impulse sequence.

See impulse for details.

Parameters:

orderint >= 0 [scalar]: Order of sequence, \(M\).
kwargsadditional keyword arguments: See torch.eye.

Returns:

xTensor [shape=(M+1,)]: Impulse sequence.

Examples

>>> x = diffsptk.impulse(4)
>>> x
tensor([1., 0., 0., 0., 0.])

diffsptk.step(order, value=1, **kwargs)[source]#

Generate step sequence.

See step for details.

Parameters:

orderint >= 0 [scalar]: Order of sequence, \(M\).
valuefloat [scalar]: Step value.
kwargsadditional keyword arguments: See torch.full.

Returns:

xTensor [shape=(M+1,)]: Step sequence.

Examples

>>> x = diffsptk.step(4, 2)
>>> x
tensor([2., 2., 2., 2., 2.])

diffsptk.ramp(arg, end=None, step=1, eps=1e-08, **kwargs)[source]#

Generate ramp sequence.

See ramp for details.

Parameters:

argfloat [scalar]: If end is None end value otherwise start value.
endfloat [scalar]: End value.
stepfloat != 0 [scalar]: Slope.
epsfloat [scalar]: A correction value.
kwargsadditional keyword arguments: See torch.arange.

Returns:

xTensor [shape=(?,)]: Ramp sequence.

Examples

>>> x = diffsptk.ramp(4)
>>> x
tensor([0., 1., 2., 3., 4.])

diffsptk.sin(order, period=None, magnitude=1, **kwargs)[source]#

Generate sinusoidal sequence.

See sin for details.

Parameters:

orderint >= 0 [scalar]: Order of sequence, \(M\).
periodfloat > 0 [scalar]: Period.
magnitudefloat [scalar]: Magnitude.
kwargsadditional keyword arguments: See torch.arange.

Returns:

xTensor [shape=(M+1,)]: Sinusoidal sequence.

Examples

>>> x = diffsptk.sin(4)
>>> x
tensor([ 0.0000,  0.9511,  0.5878, -0.5878, -0.9511])

diffsptk.train(order, frame_period, norm='power', **kwargs)[source]#

Generate pulse sequence.

See train for details.

Parameters:

orderint >= 0 [scalar]: Order of sequence, \(M\).
frame_periodfloat >= 1 [scalar]: Frame period.
norm[‘none’, ‘power’, ‘magnitude’]: Normalization type.
**kwargsadditional keyword arguments: See torch.zeros.

Returns:

xTensor [shape=(M+1,)]: Pulse sequence.

Examples

>>> x = diffsptk.train(5, 2.3)
>>> x
tensor([1.5166, 0.0000, 0.0000, 1.5166, 0.0000, 1.5166])

diffsptk.nrand(*order, mean=0, stdv=1, var=None, **kwargs)[source]#

Generate random number sequence.

See nrand for details.

Parameters:

orderint >= 0 [scalar]: Order of sequence, \(M\).
meanfloat [scalar]: Mean.
stdvfloat >= 0 [scalar]: Standard deviation.
varfloat >= 0 [scalar]: Variance.
**kwargsadditional keyword arguments: See torch.randn.

Returns:

xTensor [shape=(M+1,)]: Random value sequence.

Examples

>>> x = diffsptk.nrand(4)
>>> x
tensor([-0.8603,  0.6743, -0.9178,  1.5382, -0.2574])
>>> x = diffsptk.nrand(2, 4)
>>> x
tensor([[-0.2385, -0.0778, -0.0418, -1.6217,  0.1560],
        [ 1.6646,  0.8429,  0.9357, -0.5123,  0.9571]])