Modeling of nonlinear audio effects with end-to-end deep neural networks
This work addresses the need for versatile audio effect modeling in music production, though it appears incremental as it builds on existing deep learning approaches.
The paper tackled the problem of modeling nonlinear audio effects for music production by developing a general-purpose end-to-end deep neural network, showing it can model various nonlinearities and generalize across different instruments.
In the context of music production, distortion effects are mainly used for aesthetic reasons and are usually applied to electric musical instruments. Most existing methods for nonlinear modeling are often either simplified or optimized to a very specific circuit. In this work, we investigate deep learning architectures for audio processing and we aim to find a general purpose end-to-end deep neural network to perform modeling of nonlinear audio effects. We show the network modeling various nonlinearities and we discuss the generalization capabilities among different instruments.