Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations
This work addresses virtual analog modeling for audio effects, offering an incremental improvement in efficiency and flexibility for circuit simulation.
The paper tackled modeling distortion circuits by adapting neural ordinary differential equations to learn the ODEs governing diode clipper circuits, achieving performance comparable to state-of-the-art RNNs with fewer parameters and enabling increased sampling rates post-training for better accuracy.
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.