用音乐内在特征检测AI生成歌曲，更准更鲁棒。
Beyond Artifacts: Towards Generalizable Synthetic Song Detection via Music-Intrinsic Features
- 通过专家模型提取音乐内在特征，动态融合提升检测能力。
- 在MUSIC8K数据集上相较最强基线F1提升18.5点。
- 适合关注AI音乐检测、生成内容溯源的研究者。
AI音乐生成技术快速发展，亟需可靠的合成歌曲检测方法。现有方法多依赖低层伪影或固定特征假设，难以捕捉跨生成器的通用线索。为此，我们提出Sofia（基于音乐特征的合成歌曲检测框架），通过特征专用专家与自适应混合专家（MoE）模块，建模音乐内在属性。配置声乐、音频效果、全局结构特征及其组合，分析其独立与互补贡献。为全面评估，构建了MUSIC8K基准数据集，包含最新生成器及真实音频扰动。实验表明，Sofia从音乐内在特征中学习到生成器无关表征，在MUSIC8K-O上比最强基线F1提升18.5点，同时保持强鲁棒性。
原文摘要 · Abstract (English)
The rapid advancement of AI music generators highlights the urgent need for reliable Synthetic Song Detection (SSD). Existing SSD methods often rely on low-level artifacts or fixed feature assumptions, struggling to capture generator-agnostic cues. To address this, we propose Sofia (Synthetic-song detection framework via music features), a flexible framework that models music-intrinsic attributes via feature-specific experts and an adaptive Mixture-of-Experts (MoE) module. By configuring Sofia with representative Vocal, Audio-effect, Global structure features, and their combinations, we present their individual and complementary contributions. To comprehensively evaluate our framework, we further construct MUSIC8K, a challenging benchmark featuring lastest emerging generators and realistic audio perturbations. Experiments show that Sofia learns generator-agnostic representations from music-intrinsic features, improving the F1 score by 18.5 points over the strongest baseline on MUSIC8K-O while maintaining strong robustness.
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