APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the explosive rise of AI-generated music platforms has created an entirely new and largely unexplored landscape, where a surge of songs is produced and consumed daily without the traditional markers of artist reputation or label backing. Key, yet unexplored in this pursuit is aesthetic quality. We propose APEX, the first large-scale multi-task learning framework for AI-generated music, trained on over 211k songs (10k hours of audio) from Suno and Udio, that jointly predicts engagement-based popularity signals - streams and likes scores - alongside...
| Authors | Jaavid Aktar Husain, Dorien Herremans |
|---|---|
| Primary category | cs.SD |
| Published | 5 May 2026 |
| Last updated at source | 5 June 2026 |
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