Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
Bo-Yu Chen, Wei-Han Hsu, Wei-Hsiang Liao, Marco A. Martínez Ramírez, Yuki Mitsufuji, Yi-Hsuan Yang
Abstract
A central task of a Disc Jockey (DJ) is to create a mixset of music with seamless transitions between adjacent tracks. In this paper, we explore a data-driven approach that uses a generative adversarial network to create the song transition by learning from real-world DJ mixes. The generator uses two differentiable digital signal processing components, an equalizer (EQ) and a fader, to mix two tracks selected by a data generation pipeline. The generator has to set the parameters of the EQs and fader in such a way that the resulting mix resembles real mixes created by human DJ, as judged by the discriminator counterpart. Result of a listening test shows that the model can achieve competitive results compared with a number of baselines.
BibTeX
@inproceedings{icassp2022_automaticdjtrans,
title = {Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks},
author = {Bo-Yu Chen and Wei-Han Hsu and Wei-Hsiang Liao and Marco A. Martínez Ramírez and Yuki Mitsufuji and Yi-Hsuan Yang},
booktitle = {ICASSP 2022},
year = {2022}
}