AI 中文总结
本文提出首个混合人机音乐中AI生成音轨的检测研究，提出并行架构结合源分离与音轨专用分类器，在MUSDB18-HQ数据库上取得了令人鼓舞的检测效果。
AI 中文摘要
据我们所知，本文是首个关于检测由人类制作音轨与AI生成音轨混合而成的人机混合音乐曲目的研究。近期研究表明，AI音乐检测器可识别完全生成音乐中与解码器相关的伪影，基于此，我们探究这些伪影在混合后的音轨层面是否仍可检测。我们在由人声+伴奏双音轨构成的MUSDB18-HQ数据库中，用神经编解码器对单个音轨进行自编码以模拟混合曲目。我们对比了两种结合AI生成混合曲检测与源分离的策略：一种是朴素的顺序流程，即先进行源分离再对分离后的源进行检测，结果证实通用源分离系统无法可靠恢复与AI生成音轨相关的伪影；因此我们提出一种并行架构，其中源分离仅用于估计混合曲内的源相对能量，随后我们训练简单的音轨专用二分类器，其输入为生成混合曲预测结果及目标音轨在短音频块上的相对能量，对音频块级预测取平均后得到了令人鼓舞的曲目级结果，凸显了此类方法在检测混合音乐中AI生成音轨方面的潜力。
英文摘要
This paper presents, to the best of our knowledge, the first study on detecting human-AI hybrid music tracks created by mixing human-produced and AI-generated stems. Building on recent work showing that AI music detectors can identify decoder-related artifacts in fully generated music, we investigate whether such artifacts remain detectable at the stem level after mixing. Using MUSDB18-HQ database in a two-stem vocals + accompaniment setting, we simulate hybrid mixtures by autoencoding individual stems with a neural codec. We compare two strategies combining AI-generated mix detection and source separation. A naive sequential pipeline, where source separation is followed by detection on separated sources, confirms that artifacts associated with an AI-generated stem are not reliably recovered by generic source separation systems. We therefore propose a parallel architecture in which source separation is only used to estimate source-relative energy within the mixture. We then train simple stem-specific binary classifiers that take as input the generated mix prediction together with the relative energy of the target stem on short audio chunks. Averaging chunk-level predictions yields encouraging track-level results, highlighting the potential of such approaches for detecting AI-generated stems in hybrid music.
CommentsAccepted at ISMIR 2026
