Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations

Jan Wilczek, Alec Wright, Vesa Välimäki, Emanuël A.P. Habets

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)
25 Downloads (Pure)

Abstract

Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increased accuracy. Using a sophisticated numerical solver allows to increase the accuracy at the cost of slower processing. ODEs learned this way do not require closed forms but are still physically interpretable.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22)
EditorsGianpaolo Evangelista, Nicki Holighaus
Place of PublicationVienna, Austria
PublisherDAFx
Pages9-16
Number of pages8
Edition2022
ISBN (Electronic)978-3-200-08599-2
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventInternational Conference on Digital Audio Effects - University of Music and Performing Arts Vienna, Vienna, Austria
Duration: 7 Sept 20229 Sept 2022
Conference number: 25
https://dafx2020.mdw.ac.at/DAFx20in22/
https://dafx2020.mdw.ac.at/DAFx20in22/index.html

Publication series

NameProceedings of the International Conference on Digital Audio Effects
ISSN (Print)2413-6700
ISSN (Electronic)2413-6689

Conference

ConferenceInternational Conference on Digital Audio Effects
Abbreviated titleDAFx
Country/TerritoryAustria
CityVienna
Period07/09/202209/09/2022
Internet address

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