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Temporal Considerations in DJ Mix Information Retrieval and Generation (Short Paper)

Authors Alexander Williams , Gregor Meehan , Stefan Lattner , Johan Pauwels , Mathieu Barthet

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Subject Classification

ACM Subject Classification
  • Applied computing → Sound and music computing
  • Computing methodologies → Control methods
  • Computing methodologies → Planning under uncertainty
Keywords
  • Music Information Retrieval
  • Computational Creativity
  • Recommender Systems
  • Electronic Dance Music
  • DJ

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Abstract

Music is the art of arranging sounds in time so as to produce a continuous, unified, and evocative composition. Electronic dance music (EDM) is a collection of musical sub-genres produced using computers and electronic instruments and often presented through the medium of DJing, where tracks are curated and mixed sequentially into a continuous stream of music to offer unique listening and dancing experiences over time periods ranging from several minutes to several hours. A DJ’s actions and decisions occur at several levels of temporal granularity, from real-time audio manipulation (e.g. of tempo) for smooth inter-track transitions to long-term planning of track selection and sequencing for mix content and flow. While human DJs can instinctively operate across these different temporal resolutions, replicating this capability in an end-to-end automated DJing system presents significant challenges. In this paper, we analyse existing works in DJ mix information retrieval and generation from this temporal perspective. We first explain the close link between DJing and the temporal notion of musical rhythm, then describe a framework for categorising DJing actions by temporal granularity. Using this framework, we summarise and contrast potential approaches for automating and augmenting sequential DJ decision making, and discuss the unique characteristics of DJ mix track selection as a sequential recommendation task. In doing so, we hope to facilitate the implementation of more robust and complete automated DJing systems in future research.

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Alexander Williams, Gregor Meehan, Stefan Lattner, Johan Pauwels, and Mathieu Barthet. Temporal Considerations in DJ Mix Information Retrieval and Generation (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 20:1-20:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.TIME.2025.20

Author Details

Alexander Williams
  • Centre for Digital Music, Queen Mary University of London, United Kingdom
Gregor Meehan
  • Centre for Digital Music, Queen Mary University of London, United Kingdom
Stefan Lattner
  • Sony CSL, Paris, France
Johan Pauwels
  • Centre for Digital Music, Queen Mary University of London, United Kingdom
Mathieu Barthet
  • Aix-Marseille Univ CNRS PRISM, France
  • Centre for Digital Music, Queen Mary University of London, United Kingdom

Funding

Alexander Williams and Gregor Meehan are research students at the UKRI Centre for Doctoral Training in Artificial Intelligence and Music, supported by UK Research and Innovation [grant number EP/S022694/1] and Queen Mary University of London. Alexander Williams is also supported by Sony CSL.

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