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AFxResearch

scientific literature about audio effects

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Paper | Website

Marco Comunità, Joshua D. Reiss

Centre for Digital Music, Queen Mary University of London, UK

Citation

If you find this work useful, please consider citing us:

@article{comunita2024afx,
  title={AFX-Research: an Extensive and Flexible Repository of Research about Audio Effects},
  author={Comunita, Marco and Reiss, J},
  year={2024}
}

Support

To show your support please consider giving this repo a star ⭐.

Thanks! 🤘

Project

This repo is used to manage a database of scientific literature hosted at:

https://mcomunita.github.io/afx-research

please note the link will redirect you to a Notion ™️ web page

I chose to use Notion ™️ since it allows to visualize a dynamic table with filtering, ordering, tagging options. It's also easy to update when new literature comes out.

The papers are on topics like:

  • audio effects modelling
  • audio effects classification and parameters estimation
  • audio effects removal
  • audio effects circuits emulation
  • differentiable and non-differentiable methods
  • white-, gray- and black-box approaches to audio effects modelling
  • approaches based on: neural networks, differentiable digital signal processing, waveshaping, wave digital filters, dynamic convolution, Wiener-Hammerstein models, Volterra series, State-spaces...
  • literature reviews

Contributions

We invite anyone to contribute to this collection by submitting a new issue for each publication you would like to include.

Here is an example of table entry:


To signal a publication, simply open a new issue (there is a template for it) including as much info as possible about it:

[*] = required

  • [*] Title: title of the publication
  • [*] Author(s): author(s) of the publication
  • [*] URL: URL to the publication
  • [*] Date: in the YYYY-MM format
  • [*] Main Task: classification, estimation, modeling, processing, removal, style transfer, review
  • Paradigm(s): what paradigm(s) is the publication using (i.e., Black-, Gray-, White-box)
  • [*] Device(s) Type(s): what type of effects the publication is about (e.g., reverb, delay)
  • Device(s)s: what specific devices/circuits have been modelled (e.g., Ibanez Tube Screamer or Vacuum Tube Stage)
  • Parametric/Controllable: Y/N - whether the publication includes some sort of controllability
  • [*] Neural/Differentiable: Y/N - whether or not a differentiable approach was used
  • Method: which method(s) or combination of methods is the publication based on (e.g., Neural Network, Wiener-Hammerstein or State-space)
  • Webpage: URL of the page associated with the publication
  • Code: URL of the repo associated with the publication
  • Dataset: URL of the data associated with the publication
  • [*] Abstract

Here's an example of the info associated to each publication:

Improvements

If you have suggestions or would like to help managing the repo feel free to reach out.