dfs#

class diffsptk.InfiniteImpulseResponseDigitalFilter(b=None, a=None, ir_length=None, learnable=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)[source]#

Apply an IIR digital filter to the input waveform.

Parameters:

Returns:

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, b=None, a=None, ir_length=None)[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: