The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization

A research paper from arXiv investigates the phenomenon of musical homogenization in large-scale generative AI music systems. The study, auditing commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal), compares AI-generated tracks against human-produced music. The core objective is to determine if AI systems exhibit reduced acoustic variation in standard computational audio features, both within and across genres, and to explore the justice implications of such homogenization. The analysis utilizes 72 music information retrieval (MIR) features to assess dispersion, redundancy, and separability.

This research is critical for understanding the evolving landscape of AI-generated content and its impact on creative industries. Identifying potential homogenization in AI music systems highlights challenges to artistic diversity and cultural expression, necessitating strategic consideration for future development and deployment of such technologies.

The study audits two commercial generative music systems, Suno and Lyria 3.

Forward consideration, not a verified fact.