The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization
arXiv:2608.06106v1 Announce Type: new Abstract: This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account of why this matters. We audit two commercially deployed systems (Suno and Lyria 3) across four genres (Afrobeats, K-pop, Dance Pop, and Heavy Metal). For each system and genre, we generate 100 tracks and compare them against human corpora of equal size, using 72 music info
Record details
Published: 7 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Transparency
Retrieved: 7 August 2026
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ethics.ai (7 August 2026), “The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization,” evidence record 17014, https://ethics.ai/record/17014 (originally published by arXiv cs.CY).
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