audio_filters.iir_filter

Classes

IIRFilter

N-Order IIR filter

Module Contents

class audio_filters.iir_filter.IIRFilter(order: int)

N-Order IIR filter Assumes working with float samples normalized on [-1, 1]

—

Implementation details: Based on the 2nd-order function from

this generalized N-order function was made.

Using the following transfer function H(z)=frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+…+b_{k}z^{-k}}{a_{0}+a_{1}z^{-1}+a_{2}z^{-2}+…+a_{k}z^{-k}} we can rewrite this to y[n]={frac{1}{a_{0}}}left(left(b_{0}x[n]+b_{1}x[n-1]+b_{2}x[n-2]+…+b_{k}x[n-k]right)-left(a_{1}y[n-1]+a_{2}y[n-2]+…+a_{k}y[n-k]right)right)

process(sample: float) → float

Calculate y[n]

>>> filt = IIRFilter(2)
>>> filt.process(0)
0.0
set_coefficients(a_coeffs: list[float], b_coeffs: list[float]) → None

Set the coefficients for the IIR filter. These should both be of size order + 1. a_0 may be left out, and it will use 1.0 as default value.

This method works well with scipy’s filter design functions
>>> # Make a 2nd-order 1000Hz butterworth lowpass filter
>>> import scipy.signal
>>> b_coeffs, a_coeffs = scipy.signal.butter(2, 1000,
...                                          btype='lowpass',
...                                          fs=48000)
>>> filt = IIRFilter(2)
>>> filt.set_coefficients(a_coeffs, b_coeffs)
a_coeffs
b_coeffs
input_history
order
output_history