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

TagLab: an image segmentation tool oriented to marine data analysis

PyTorch implementation of residual gated graph ConvNets, ICLR’18

Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

这是一个利用facenet和retinaface实现人脸识别的库，可以进行在线的人脸识别。

Dieser Scanner findet Websites, die nicht direkt in Suchmaschinen auftauchen, aber trotzdem erreichbar sind.

Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses‘

Code for CVPR 2018 paper --- Texture Mapping for 3D Reconstruction with RGB-D Sensor

A large-image collection explorer and fast classification tool

Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Liecasadi - liecasadi implements Lie groups operation written in CasADi

Prototype python implementation of the ome-ngff table spec

A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.

Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Implementation of the pix2pix model on satellite images

Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

Create Data & AI apps in 20 lines of code with Shimoku

This repository contains the code used for the implementation of the paper "Probabilistic Regression with HuberDistributions"

Source code of our work: "Benchmarking Deep Models for Salient Object Detection"