构建真实电视广播数据集，揭示现有AI音乐检测方法在实际场景中失效。
Assessing AI-generated music detection in real-world broadcast monitoring
- 基于真实电视录像构建40小时广播音乐数据集BAMM
- CNN模型在真实广播场景下准确率大幅下降，真假音乐分数重叠严重
- 广播环境训练虽有改善，但仍无法满足可靠检测需求
AI生成音乐在广播媒体中的泛滥引发透明度与公平补偿的担忧，但真实广播条件下的可靠检测仍无解。现有研究虽报告性能显著下降，但评估仅限于合成广播数据。为填补这一空白，我们提出BAMM（Broadcast AI-Music Monitoring）——一个包含40小时真实电视录播的音频数据集，涵盖AI生成与人工创作音乐。我们在三个逐步加剧的场景中对比了纯净训练与广播训练的CNN模型：纯净前景音乐（CFM）、合成电视广播（STB）和真实电视广播（RTB）。两者在CFM上表现接近完美，但在合成广播条件下性能显著下降。广播定向训练提升了鲁棒性，但效果仍有限。在真实广播场景（使用BAMM评估）中，两类模型进一步退化，且AI生成与人工音乐得分出现明显重叠。结果揭示了关键领域差距，表明当前基于CNN的检测方法在广播监控中仍不足以实现可靠识别。
原文摘要 · Abstract (English)
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 music. We compare clean-trained and broadcast-trained CNN variants across three progressively more challenging scenarios: Clean Foreground Music (CFM), Synthetic TV Broadcast (STB), and Real TV Broadcast (RTB). Both models achieve near-perfect performance on CFM but degrade substantially under synthetic broadcast conditions. Broadcast-oriented training improves robustness compared with clean training, although performance remains limited. On RTB, evaluated using BAMM, both models degrade further and show substantial score overlap between AI-generated and human-made music. These results expose a critical domain gap and show that current training approaches on CNN-based detectors remain insufficient for reliable AI-generated music detection in broadcast monitoring.
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