Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
Sound
2018-05-03 v2 Machine Learning
Multimedia
Audio and Speech Processing
Abstract
We describe a system based on deep learning that generates drum patterns in the electronic dance music domain. Experimental results reveal that generated patterns can be employed to produce musically sound and creative transitions between different genres, and that the process of generation is of interest to practitioners in the field.
Cite
@article{arxiv.1804.09808,
title = {Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres},
author = {Tijn Borghuis and Alessandro Tibo and Simone Conforti and Luca Canciello and Lorenzo Brusci and Paolo Frasconi},
journal= {arXiv preprint arXiv:1804.09808},
year = {2018}
}