{
  "id": 17014,
  "url": "https://arxiv.org/abs/2608.06106",
  "title": "The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization",
  "summary": "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",
  "authors": "Zoe Slendebroek, Dana\\'e Metaxa",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T04:00:00.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17014",
  "original_url": "https://arxiv.org/abs/2608.06106",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}