用AI模拟人对音乐抄袭的判断，找出关键感知特征。
Understanding Human Perception of Music Plagiarism Through a Computational Approach
- 构建LLM评判框架，分步分析旋律、节奏、和弦等特征。
- 发现人类感知相似性主要依赖旋律与和弦变化模式。
- 适合音乐版权研究者与AI作曲工具开发者参考。
存在多种音乐相似性检测算法，但现实中的音乐抄袭讨论往往基于听众感知。因此，本文旨在通过计算方法研究人类对音乐抄袭感知的关键标准，重点关注相似性分析中常用的三个音乐特征：旋律、节奏和和弦进行。在识别出人类感知音乐相似性所依赖的关键特征及变化层次后，提出一种基于大语言模型的评判框架，采用系统化、分步骤的方法，结合提取这些高层属性的模块，实现对音乐抄袭感知的量化分析。
原文摘要 · Abstract (English)
There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, focusing on the three commonly used musical features in similarity analysis: melody, rhythm, and chord progression. After identifying the key features and levels of variation humans use in perceiving musical similarity, we propose a LLM-as-a-judge framework that applies a systematic, step-by-step approach, drawing on modules that extract such high-level attributes.
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