{
  "id": 17800,
  "url": "https://arxiv.org/abs/2608.07359v1",
  "title": "Assessing AI-generated music detection in real-world broadcast monitoring",
  "summary": "The proliferation of AI-generated music in broadcast media raises concerns about transparency and fair compensation, but reliable detection under real broadcast conditions remains unresolved. Existing studies report substantial performance degradation in this domain, yet their evaluations are limited to synthetic broadcast data. To address this gap, we introduce BAMM (Broadcast AI-Music Monitoring), a 40-hour dataset of real-world television recordings containing AI-generated and human-made musi",
  "authors": "David López-Ayala, Fernando García de la Cruz, Pablo Zinemanas, Emilio Molina, Martín Rocamora",
  "category": "research",
  "topics": "transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T15:58:04.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17800",
  "original_url": "https://arxiv.org/abs/2608.07359v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}