IIRNet
Direct design of biquad filter cascades with deep learning by sampling random polynomials.
Usage
git clone https://github.com/csteinmetz1/IIRNet.git
pip install .
Filter design
Start designing filters with just a few lines of code. In this example (demos/basic.py ) we create a 32nd order IIR filter to match an arbitrary response that we define over a few points. Internally, this specification will be interpolated to 512 points.
import torch
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
from iirnet.designer import Designer
# first load IIRNet with pre-trained weights
designer = Designer()
n = 32 # Desired filter order (4, 8, 16, 32, 64)
m = [0, -3, 0, 12, 0, -6, 0] # Magnitude response specification
mode = "linear" # interpolation mode for specification
output = "sos" # Output type ("sos", or "ba")
# now call the designer with parameters
sos = designer(n, m, mode=mode, output=output)
# measure and plot the response
w, h = scipy.signal.sosfreqz(sos.numpy(), fs=2)
# interpolate the target for plotting
m_int = torch.tensor(m).view(1, 1, -1).float()
m_int = torch.nn.functional.interpolate(m_int, 512, mode=mode)
fig, ax = plt.subplots(figsize=(6, 3))
plt.plot(w, 20 * np.log10(np.abs(h)), label="Estimation")
plt.plot(w, m_int.view(-1), label="Specification")
# .... more plotting ....
See demos/basic.py for the full script.
Training
We provide a set of shell scripts that will launch training jobs that reproduce the experiments from the paper in configs/. These should be launched from the top level after installing.
./configs/train_hidden_dim.sh
./configs/filter_method.sh
./configs/filter_order.sh
Evaluation
Running the evaluation will require both the pre-trained models (or models you trained yourself) along with the HRTF and Guitar cabinet datasets. These datasets can be downloaded as follows:
First, change to the data directory and then run the download script.
cd data
./dl.sh
Note, you may need to install 7z if you don't already have it. brew install p7zip on macOS
Next download the pre-trained checkpoints if you haven't already.
mkdir logs
cd logs
wget https://zenodo.org/record/5550275/files/filter_method.zip
wget https://zenodo.org/record/5550275/files/filter_order.zip
wget https://zenodo.org/record/5550275/files/hidden_dim.zip
unzip filter_method.zip
unzip filter_order.zip
unzip hidden_dim.zip
rm filter_method.zip
rm filter_order.zip
rm hidden_dim.zip
Now you can run the evaluation on checkpoints from the three different experiments as follows.
python eval.py logs/filter_method --yw --sgd --guitar_cab --hrtf --filter_order 16
python eval.py logs/hidden_dim --yw --sgd --guitar_cab --hrtf --filter_order 16
For the filter order experiment we need to run the eval script across all models for every order.
python eval.py logs/filter_order --guitar_cab --hrtf --filter_order 4
python eval.py logs/filter_order --guitar_cab --hrtf --filter_order 8
python eval.py logs/filter_order --guitar_cab --hrtf --filter_order 16
python eval.py logs/filter_order --guitar_cab --hrtf --filter_order 32
python eval.py logs/filter_order --guitar_cab --hrtf --filter_order 64
Note: Requires PyTorch >=1.8
Filter methods
| ID | Sampling method | Name |
|---|---|---|
| (A) | Normal coefficients | normal_poly |
| (B) | Normal biquads | normal_biquad |
| (C) | Uniform disk | uniform_disk |
| (D) | Uniform magnitude disk | uniform_mag_disk |
| (E) | Characteristic | char_poly |
| (F) | Uniform parametric | uniform_parametric |
Citation
@article{colonel2021iirnet,
title={Direct design of biquad filter cascades with deep learning by sampling random polynomials},
author={Colonel, Joseph and Steinmetz, Christian J. and Michelen, Marcus and Reiss, Joshua D.},
booktitle={arXiv:2110.03691},
year={2021}}
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