APEX: New Framework Predicts Popularity of AI-Generated Music Using Aesthetic Quality Metrics
Researchers have developed APEX, a machine learning framework trained on over 211,000 AI-generated songs that predicts music popularity by analyzing both engagement metrics and aesthetic quality dimensions. The framework was trained on music from platforms Suno and Udio and uses a self-supervised model called MERT to extract audio features. The work addresses a gap in understanding what makes AI-generated music successful, with potential applications for artists, streaming platforms, and recommendation systems.
APEX is a multi-task learning framework designed specifically for the emerging landscape of AI-generated music, where traditional markers like artist reputation and label backing are absent. The researchers trained the system on over 211,000 songs (approximately 10,000 hours of audio) from two major AI music generation platforms, Suno and Udio. The framework simultaneously predicts two types of signals: engagement-based popularity metrics (streams and likes) and five dimensions of perceptual aesthetic quality. By combining these complementary aspects, APEX demonstrated improved performance in predicting human preferences across different generative music systems in out-of-distribution testing on the Music Arena dataset. The approach shows strong generalization capabilities, suggesting that aesthetic quality features learned from one set of AI music generators transfer effectively to others.
What's missing
The study does not discuss potential limitations of relying on engagement metrics (streams/likes) as proxies for quality, nor does it address how aesthetic preferences may vary across cultural or demographic groups. Additionally, the paper does not elaborate on the specific five aesthetic quality dimensions being measured or provide details on inter-rater reliability for human preference judgments in the Music Arena dataset.
What different sources said
- arXiv cs.AICenter
APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
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