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

Toolbox to analyze temporal context invariance of deep neural networks

Using this you can control your PC/Laptop volume by Hand Gestures (pinch-in, pinch-out) created with Python.

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

RobustVideoMatting and background composing in one model by using onnxruntime.

Churn prediction

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现

This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing described in paper Discontinuous Grammar as a Foreign Language.

Realtime YOLO Monster Detection With Non Maximum Supression

[Official] Exploring Temporal Coherence for More General Video Face Forgery Detection(ICCV 2021)

DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors

Python library for loading and using triangular meshes.

Generic image compressor for machine learning. Pytorch code for our paper "Lossy compression for lossless prediction".

Relative Uncertainty Learning for Facial Expression Recognition

A Simple Long-Tailed Rocognition Baseline via Vision-Language Model

maximal update parametrization (µP)

nanodet_plus,yolov5_v6.0

Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

一个免费开源一键搭建的通用验证码识别平台，大部分常见的中英数验证码识别都没啥问题。

This tool uses Deep Learning to help you draw and write with your hand and webcam.