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

arXiv: https://arxiv.org/abs/2110.06525 · HTML: https://mlsanity.com/paper/2110.06525/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [Automatic multitrack mixing with a differentiable mixing console of neural audio effects](https://mlsanity.com/paper/2010.10291/) (2020)
- [Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music](https://mlsanity.com/paper/2008.05216/) (2020)
- [Lightweight and interpretable neural modeling of an audio distortion effect using hyperconditioned differentiable biquads](https://mlsanity.com/paper/2103.08709/) (2021)
- [Differentiable Signal Processing With Black-Box Audio Effects](https://mlsanity.com/paper/2105.04752/) (2021)
- [Latent Space Explorations of Singing Voice Synthesis using DDSP](https://mlsanity.com/paper/2103.07197/) (2021)
- [A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment](https://mlsanity.com/paper/2008.10267/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features](https://mlsanity.com/paper/2110.08862/) (2021)
- [Cue Point Estimation using Object Detection](https://mlsanity.com/paper/2407.06823/) (2024)
- [Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models](https://mlsanity.com/paper/2211.00731/) (2022)

## Abstract (arXiv)

A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic 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. In particular, the generator of the model 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 away that the resulting mix resembles real mixes created by humanDJ, 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.