Modeling of nonlinear audio effects with end-to-end deep neural networks
Audio and Speech Processing
2019-05-10 v2 Machine Learning
Sound
Signal Processing
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.
Keywords
Cite
@article{arxiv.1810.06603,
title = {Modeling of nonlinear audio effects with end-to-end deep neural networks},
author = {Marco A. Martínez Ramirez and Joshua D. Reiss},
journal= {arXiv preprint arXiv:1810.06603},
year = {2019}
}
Comments
Presented at the 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, May 2019