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]

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Implementation details: Based on the 2nd-order function from https://en.wikipedia.org/wiki/Digital_biquad_filter, 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