Automatic Playlist Sequencing and Transitions


Professional music curators and DJs artfully arrange and mix recordings together to create engaging, seamless, and cohesive listening experiences, a craft enjoyed by audiences around the world. The average listener, however, lacks both the time and the skill necessary to create comparable experiences, despite access to same source material. As a result, user-generated listening sessions often lack the sophistication popularized by modern artists, e.g. tracks are played in their entirety with little or no thought given to their ordering. To these ends, this paper presents methods for automatically sequencing existing playlists and adding DJ-style crossfade transitions between tracks: the former is modeled as a graph traversal problem, and the latter as an optimization problem. Our approach is motivated by an analysis of listener data on a large music catalog, and subjectively evaluated by professional curators.


August 2020 | ISMIR - International Society for Music Information Retrieval Conference

Data Cleansing with Contrastive Learning for Vocal Note Event Annotations

Gabriel Meseguer-Brocal, Rachel Bittner, Simon Durand, Brian Brost

July 2020 | IJCAI - International Joint Conference on Artificial Intelligence

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