Direct design of biquad filter cascades with deep learning by sampling random polynomials.

Last update: Nov 02, 2022

Related tags

Deep Learning IIRNet

Overview

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}}

Direct design of biquad filter cascades with deep learning by sampling random polynomials.

Related tags

Overview

IIRNet

Usage

Filter design

Training

Evaluation

Filter methods

Citation

Owner

Christian J. Steinmetz

Experiments with the Robust Binary Interval Search (RBIS) algorithm, a Query-Based prediction algorithm for the Online Search problem.

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss

Official code for UnICORNN (ICML 2021)

"Learning Free Gait Transition for Quadruped Robots vis Phase-Guided Controller"

[PNAS2021] The neural architecture of language: Integrative modeling converges on predictive processing

[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"

An automated facial recognition based attendance system (desktop application)

Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021)

[ICCV21] Self-Calibrating Neural Radiance Fields

Website which uses Deep Learning to generate horror stories.

object recognition with machine learning on Respberry pi

DAN: Unfolding the Alternating Optimization for Blind Super Resolution

An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

Learning 3D Part Assembly from a Single Image

This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Gender Classification Machine Learning Model using Sk-learn in Python with 97%+ accuracy and deployment

Vehicles Counting using YOLOv4 + DeepSORT + Flask + Ngrok

🛠️ Tools for Transformers compression using Lightning ⚡

An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.