[2602.06823] AI-Generated Music Detection in Broadcast Monitoring

Computer Science > Sound arXiv:2602.06823 (cs) [Submitted on 6 Feb 2026 (v1), last revised 2 Mar 2026 (this version, v2)] Title:AI-Generated Music Detection in Broadcast Monitoring Authors:David López-Ayala, Asier Cabello, Pablo Zinemanas, Emilio Molina, Martín Rocamora View a PDF of the paper titled AI-Generated Music Detection in Broadcast Monitoring, by David L\'opez-Ayala and 4 other authors View PDF HTML (experimental) Abstract:AI music generators have advanced to the point where their outputs are often indistinguishable from human compositions. While detection methods have emerged, they are typically designed and validated in music streaming contexts with clean, full-length tracks. Broadcast audio, however, poses a different challenge: music appears as short excerpts, often masked by dominant speech, conditions under which existing detectors fail. In this work, we introduce AI-OpenBMAT, the first dataset tailored to broadcast-style AI-music detection. It contains 3,294 one-minute audio excerpts (54.9 hours) that follow the duration patterns and loudness relations of real television audio, combining human-made production music with stylistically matched continuations generated with Suno v3.5. We benchmark a CNN baseline and state-of-the-art SpectTTTra models to assess SNR and duration robustness, and evaluate on a full broadcast scenario. Across all settings, models that excel in streaming scenarios suffer substantial degradation, with F1-scores dropping below 60% whe...