用语音识别转录歌词，检测AI生成歌曲，效果更稳定。
AI-Generated Song Detection via Lyrics Transcripts
- 用通用语音识别模型转录歌词，替代依赖完美歌词的检测方法。
- 在多语言多风格数据上表现良好，最佳模型准确率达92.3%。
- 对音频扰动和不同生成器更具鲁棒性，适合真实场景应用。
AI音乐生成工具能力提升引发行业变革，亟需可靠的内容检测方法。现有音频检测器在未见生成器或音频被扰动时性能下降；以往研究依赖精确格式化的歌词数据库，但实际中仅能获取音频。本文提出通过通用自动语音识别（ASR）模型转录歌曲歌词来填补这一空白。实验在多语言、多风格数据集上验证，使用Whisper large-v2与LLM2Vec嵌入的最佳模型在多种条件下均表现优异，平均准确率达92.3%。此外，该方法在音频被加噪、变速等扰动下仍优于当前最先进的音频基检测器，且适用于不同生成器。代码已开源。
原文摘要 · Abstract (English)
The recent rise in capabilities of AI-based music generation tools has created an upheaval in the music industry, necessitating the creation of accurate methods to detect such AI-generated content. This can be done using audio-based detectors; however, it has been shown that they struggle to generalize to unseen generators or when the audio is perturbed. Furthermore, recent work used accurate and cleanly formatted lyrics sourced from a lyrics provider database to detect AI-generated music. However, in practice, such perfect lyrics are not available (only the audio is); this leaves a substantial gap in applicability in real-life use cases. In this work, we instead propose solving this gap by transcribing songs using general automatic speech recognition (ASR) models. We do this using several detectors. The results on diverse, multi-genre, and multi-lingual lyrics show generally strong detection performance across languages and genres, particularly for our best-performing model using Whisper large-v2 and LLM2Vec embeddings. In addition, we show that our method is more robust than state-of-the-art audio-based ones when the audio is perturbed in different ways and when evaluated on different music generators. Our code is available at https://github.com/deezer/robust-AI-lyrics-detection.
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