AI 中文总结
研究无人知晓输入样本生成模型时的人工智能音乐检测，提出结合伪像提取法、非负矩阵分解及分类聚类方法，用于区分真实与合成音乐及零样本多类识别，在相关任务中性能优异，可监测大规模目录。
AI 中文摘要
我们提出了一种在检测器未知生成输入样本的模型的场景中进行人工智能生成音乐检测的新方法（例如，来自新发布的服务）。自2023年以来，用户友好的人工智能音乐生成服务（如Suno、Udio）不断增加，且经常更新和有新功能。因此需要以无监督方式解决合成内容检测以适应这种快速变化的情况。该角度在音乐领域研究较少。我们提议研究两个任务。一是区分真实音乐和合成音乐，可采用单类方式，用一些基准真实音乐确定范围外的内容。二是零样本多类识别，类似于对真实音乐和各种人工智能音乐生成的混合进行无监督聚类任务，目标是创建连贯、高纯度的聚类。我们提出了一种先前提出的伪像提取方法的组合，在此基础上应用非负矩阵分解以及简单的分类和聚类方法。我们在这两个任务上都取得了优异性能，表明所提方法可用于监测可能收到来自各种新发布生成模型的人工智能生成样本的大规模目录。
英文摘要
We present a novel method for AI-generated music detection in scenarios where the models that generated the input samples are unknown to the detector (e.g., from a newly released service). Since 2023, there has been a multiplication of user-friendly AI-music generation services (e.g., Suno, Udio), along with regular updates and new features. There is thus a need to address synthetic content detection in an unsupervised way to adapt to this rapidly changing context. This angle has not been much studied in music yet. We propose to study two tasks. First, discriminating between real and synthetic music. This may be approached in a one-class manner, namely, using some baseline real music and trying to determine what falls outside. Second, zero-shot multi-class identification, which is more similar to an unsupervised clustering task on a mix of real and various AI-music generations, where the goal is to create coherent, high-purity clusters. We propose a combination of a previously proposed artifact-extraction method, on top of which we apply non-negative matrix factorization and simple classification and clustering methods. We achieve excellent performance on both tasks, showing that the proposed methods may be used to monitor large-scale catalogs that may receive AI-generated samples from various newly released generative models.
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