dfs#
- diffsptk.IIR#
alias of
InfiniteImpulseResponseDigitalFilter
- class diffsptk.InfiniteImpulseResponseDigitalFilter(b: list[T] | tuple[T, ...] | ndarray | None = None, a: list[T] | tuple[T, ...] | ndarray | None = None, ir_length: int | None = None, learnable: bool = False)[source]#
See this page for details.
- Parameters:
- bList [shape=(M+1,)] or None
The numerator coefficients.
- aList [shape=(N+1,)] or None
The denominator coefficients.
- ir_lengthint >= 1 or None
The length of the truncated impulse response. If given, the filter is approximated by an FIR filter.
- learnablebool
If True, the filter coefficients are learnable.
- forward(x: Tensor) Tensor[source]#
Apply an IIR digital filter to the input waveform.
- Parameters:
- xTensor [shape=(…, T)]
The input waveform.
- Returns:
- outTensor [shape=(…, T)]
The filtered waveform.
Examples
>>> x = diffsptk.ramp(4) >>> dfs = diffsptk.IIR(b=[1, -0.97]) >>> y = dfs(x) >>> y tensor([0.0000, 1.0000, 1.0300, 1.0600, 1.0900])
- diffsptk.functional.dfs(x: Tensor, b: Tensor | None = None, a: Tensor | None = None, ir_length: int | None = None) Tensor[source]#
Apply an IIR digital filter to the input waveform.
- Parameters:
- xTensor [shape=(…, T)]
The input waveform.
- bTensor [shape=(M+1,)] or None
The numerator coefficients.
- aTensor [shape=(N+1,)] or None
The denominator coefficients.
- ir_lengthint >= 1 or None
The length of the truncated impulse response. If given, the filter is approximated by an FIR filter.
- Returns:
- outTensor [shape=(…, T)]
The filtered waveform.
See also