signals#
- diffsptk.impulse(order: int, **kwargs) Tensor[source]#
Generate impulse sequence.
See impulse for details.
- Parameters:
- orderint >= 0
The order of the sequence, \(M\).
- **kwargsadditional keyword arguments
See torch.eye.
- Returns:
- outTensor [shape=(M+1,)]
The impulse sequence.
Examples
>>> x = diffsptk.impulse(4) >>> x tensor([1., 0., 0., 0., 0.])
- diffsptk.step(order: int, value: float = 1, **kwargs) Tensor[source]#
Generate step sequence.
See step for details.
- Parameters:
- orderint >= 0
The order of the sequence, \(M\).
- valuefloat
Step value.
- **kwargsadditional keyword arguments
See torch.full.
- Returns:
- outTensor [shape=(M+1,)]
The step sequence.
Examples
>>> x = diffsptk.step(4, 2) >>> x tensor([2., 2., 2., 2., 2.])
- diffsptk.ramp(arg: float, end: float | None = None, step: float = 1, eps: float = 1e-08, **kwargs) Tensor[source]#
Generate ramp sequence.
See ramp for details.
- Parameters:
- argfloat
If end is None, this is the end value otherwise start value.
- endfloat or None
The end value.
- stepfloat != 0
The slope.
- epsfloat
A correction value.
- **kwargsadditional keyword arguments
See torch.arange.
- Returns:
- outTensor [shape=(?,)]
The ramp sequence.
Examples
>>> x = diffsptk.ramp(4) >>> x tensor([0., 1., 2., 3., 4.])
- diffsptk.sin(order: int, period: float | None = None, magnitude: float = 1, **kwargs) Tensor[source]#
Generate sinusoidal sequence.
See sin for details.
- Parameters:
- orderint >= 0
The order of the sequence, \(M\).
- periodfloat > 0
The period.
- magnitudefloat
The magnitude.
- **kwargsadditional keyword arguments
See torch.arange.
- Returns:
- outTensor [shape=(M+1,)]
The sinusoidal sequence.
Examples
>>> x = diffsptk.sin(4) >>> x tensor([ 0.0000, 0.9511, 0.5878, -0.5878, -0.9511])
- diffsptk.train(order: int, frame_period: float, norm: str | int = 'power', **kwargs) Tensor[source]#
Generate pulse sequence.
See train for details.
- Parameters:
- orderint >= 0
The order of the sequence, \(M\).
- frame_periodfloat >= 1
The frame period.
- norm[‘none’, ‘power’, ‘magnitude’]
The normalization type.
- **kwargsadditional keyword arguments
See torch.zeros.
- Returns:
- outTensor [shape=(M+1,)]
The 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: int, mean: float = 0, stdv: float = 1, var: float | None = None, **kwargs) Tensor[source]#
Generate random number sequence.
See nrand for details.
- Parameters:
- orderint >= 0
The order of the sequence, \(M\).
- meanfloat
The mean.
- stdvfloat >= 0
The standard deviation.
- varfloat >= 0
The variance. This overrides stdv.
- **kwargsadditional keyword arguments
See torch.randn.
- Returns:
- outTensor [shape=(M+1,)]
The 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]])