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  • Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks

Research Area

Author

  • Bo-Yu Chen*, Wei-Han Hsu*, Wei-Hsiang Liao, Marco A. Martínez Ramírez, Yuki Mitsufuji, Yi-Hsuan Yang*

Company

  • Sony Group Corporation

Venue

  • ICASSP

Date

  • 2022

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Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks

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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.

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