旋律或机器：基于双流对比学习的合成音乐检测
Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning
AI总结：
本文提出MoM基准和CLAM架构，通过双流对比学习检测合成音乐，实现高精度的合成音乐识别
摘要：
端到端AI音乐生成的快速发展对艺术真实性和版权构成了日益严峻的威胁，要求检测方法能够跟上步伐。尽管基础模型如SpecTTTra在面对新生成器多样且快速发展的生态系统时表现不佳，对分布外（OOD）内容的性能显著下降。这种泛化失败突显了一个关键缺口：需要更具有挑战性的基准和更稳健的检测架构。为此，我们首先引入Melody or Machine（MoM），一个包含超过13万首歌曲（6,665小时）的新大规模基准。MoM是迄今为止最多样化的数据集，由开源和闭源模型混合构建，并且包含精心挑选的OOD测试集，专门用于促进真正可泛化的检测器的发展。与此同时，我们引入了CLAM，一种新的双流检测架构。我们假设微妙的机器诱导的不一致，往往在混合信号中难以察觉，是合成的有力标志。CLAM被设计用来测试这一假设，通过使用两个预训练的音频编码器（MERT和Wave2Vec2）来创建音频的并行表示。这些表示通过一个可学习的交叉聚合模块进行融合，该模块建模它们的相互依赖性。模型通过双损失目标进行训练：标准二元交叉熵损失用于分类，补充对比三元组损失，该损失训练模型区分一致和人工不匹配的流对，增强其对合成伪影的敏感性，而无需假设简单的特征对齐。CLAM在合成音乐取证领域建立了新的状态-of-the-art。它在我们具有挑战性的MoM基准上实现了0.925的F1分数。
英文摘要：
The rapid evolution of end-to-end AI music generation poses an escalating threat to artistic authenticity and copyright, demanding detection methods that can keep pace. While foundational, existing models like SpecTTTra falter when faced with the diverse and rapidly advancing ecosystem of new generators, exhibiting significant performance drops on out-of-distribution (OOD) content. This generalization failure highlights a critical gap: the need for more challenging benchmarks and more robust detection architectures. To address this, we first introduce Melody or Machine (MoM), a new large-scale benchmark of over 130,000 songs (6,665 hours). MoM is the most diverse dataset to date, built with a mix of open and closed-source models and a curated OOD test set designed specifically to foster the development of truly generalizable detectors. Alongside this benchmark, we introduce CLAM, a novel dual-stream detection architecture. We hypothesize that subtle, machine-induced inconsistencies between vocal and instrumental elements, often imperceptible in a mixed signal, offer a powerful tell-tale sign of synthesis. CLAM is designed to test this hypothesis by employing two distinct pre-trained audio encoders (MERT and Wave2Vec2) to create parallel representations of the audio. These representations are fused by a learnable cross-aggregation module that models their inter-dependencies. The model is trained with a dual-loss objective: a standard binary cross-entropy loss for classification, complemented by a contrastive triplet loss which trains the model to distinguish between coherent and artificially mismatched stream pairings, enhancing its sensitivity to synthetic artifacts without presuming a simple feature alignment. CLAM establishes a new state-of-the-art in synthetic music forensics. It achieves an F1 score of 0.925 on our challenging MoM benchmark.
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。
