Double Entendre：基于多视角融合的鲁棒音频AI生成歌词检测
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
AI总结：
提出多模态后期融合管道DE-detect，结合自动转录歌词与语音特征，解决AI生成音乐检测中泛化差和依赖清晰歌词的问题，提升鲁棒性。
摘要：
基于AI的音乐生成工具的快速发展正在彻底改变音乐产业，但也给艺术家、版权持有者和提供商带来了挑战。这就需要可靠的方法来检测此类AI生成的内容。然而，现有的检测器仅依赖音频或歌词，面临关键的实际局限性：基于音频的检测器无法泛化到新的或未见过的生成器，且易受音频扰动影响；基于歌词的方法需要格式清晰且准确的歌词，而这在实际中无法获得。为了克服这些局限性，我们提出了一种新颖的、具有实际基础的方法：一种多模态、模块化的后期融合管道，结合了自动转录的演唱歌词和捕获音频中与歌词相关信息的语音特征。通过直接从音频中提取歌词特征，我们的方法增强了鲁棒性，降低了对低层伪影的敏感性，并实现了实际适用性。实验表明，我们的方法DE-detect优于现有的基于歌词的检测器，同时对音频扰动更具鲁棒性。因此，它为在真实场景中检测AI生成音乐提供了一种有效且鲁棒的解决方案。代码可在https://github.com/deezer/robust-AI-lyrics-detection获取。
英文摘要：
The rapid advancement of AI-based music generation tools is revolutionizing the music industry but also posing challenges to artists, copyright holders, and providers alike. This necessitates reliable methods for detecting such AI-generated content. However, existing detectors, relying on either audio or lyrics, face key practical limitations: audio-based detectors fail to generalize to new or unseen generators and are vulnerable to audio perturbations; lyrics-based methods require cleanly formatted and accurate lyrics, unavailable in practice. To overcome these limitations, we propose a novel, practically grounded approach: a multimodal, modular late-fusion pipeline that combines automatically transcribed sung lyrics and speech features capturing lyrics-related information within the audio. By relying on lyrical aspects directly from audio, our method enhances robustness, mitigates susceptibility to low-level artifacts, and enables practical applicability. Experiments show that our method, DE-detect, outperforms existing lyrics-based detectors while also being more robust to audio perturbations. Thus, it offers an effective, robust solution for detecting AI-generated music in real-world scenarios. Our code is available at https://github.com/deezer/robust-AI-lyrics-detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
