Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations

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Journal Title
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Conference article in proceedings
Date
2022
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Language
en
Pages
8
9-16
Series
Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22), issue 2022, Proceedings of the International Conference on Digital Audio Effects
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.
Description
Funding Information: ∗ This work was supported by a fellowship within the IFI programme of the German Academic Exchange Service (DAAD). † This research is part of the activities of the Nordic Sound and Music Computing Network—NordicSMC (NordForsk project no. 86892). ‡A joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits (IIS). Publisher Copyright: Copyright: © 2022 Jan Wilczek et al.
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Citation
Wilczek , J , Wright , A , Välimäki , V & Habets , E A P 2022 , Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations . in G Evangelista & N Holighaus (eds) , Proceedings of the 25th International Conference on Digital Audio Effects (DAFx20in22) . 2022 edn , 12 , Proceedings of the International Conference on Digital Audio Effects , DAFx , Vienna, Austria , pp. 9-16 , International Conference on Digital Audio Effects , Vienna , Austria , 07/09/2022 . < https://dafx2020.mdw.ac.at/proceedings/papers/DAFx20in22_paper_12.pdf >