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Citation

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

@inproceedings{comunita19afxresearch,
  title={AFxResearch: a repository and website of audio effects research},
  author={Comunita, Marco and Reiss, Joshua D},
  booktitle={DMRN+ 19: Digital Music Research Network One-day Workshop 2024}
}

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Support

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

Thanks! :metal:

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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 :tm: web page

I chose to use Notion :tm: 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

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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:

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Improvements

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