Title: Modeling of nonlinear audio effects with end-to-end deep neural networks,
Abstract: 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.