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

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

[ICCV'21] Neural Radiance Flow for 4D View Synthesis and Video Processing

Research code for the paper "Variational Gibbs inference for statistical estimation from incomplete data".

This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

A fast MoE impl for PyTorch

A solution to ensure Crowd Management with Contactless and Safe systems.

Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

Image based Human Fall Detection

This repository contains numerical implementation for the paper Intertemporal Pricing under Reference Effects: Integrating Reference Effects and Consumer Heterogeneity.

Phy-Q: A Benchmark for Physical Reasoning

This is a re-implementation of TransGAN: Two Pure Transformers Can Make One Strong GAN (CVPR 2021) in PyTorch.

A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"

Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

Fuzzing JavaScript Engines with Aspect-preserving Mutation

An open source implementation of CLIP.

A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.