Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations

TL;DR

This paper introduces neural ordinary differential equations for virtual analog distortion modeling, achieving high accuracy with fewer parameters and greater flexibility than traditional methods.

Contribution

It adapts neural ODEs to model diode clipper circuits, enabling accurate, interpretable, and parameter-efficient virtual analog modeling without oversampling.

Findings

Comparable performance to state-of-the-art RNNs

No oversampling needed, flexible sampling rate

Physically interpretable learned ODEs

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.