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.

Why it matters

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.

Key insights

• The study audits two commercial generative music systems, Suno and Lyria 3.
• Four genres were examined: Afrobeats, K-pop, Dance Pop, and Heavy Metal.
• 100 tracks per system and genre were generated and compared to human corpora of equal size.
• 72 music information retrieval (MIR) features were used for comparison.
• The research defines homogenization as reduced acoustic variation in features like rhythm, timing, timbre/spectral shape, and dynamics.
• The analysis investigates both within-genre and across-genre homogenization.
• The paper aims to develop a justice-centered account of why AI music homogenization is significant.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.06106