明确音乐抄袭检测任务定义并提出基于片段转录的解决方案
Music Plagiarism Detection: Problem Formulation and a Segment-based Solution
- 提出将音乐抄袭检测区分为独立任务，明确其与传统MIR任务的区别
- 构建Similar Music Pair数据集，支持新任务的评估与研究
- 采用片段转录方法实现精准抄袭片段匹配，适合音乐版权保护场景
近年来，音乐抄袭问题已成为更紧迫的社会议题。随着音乐信息检索（MIR）研究的推进，学界对音乐抄袭问题的关注日益增加。然而，许多研究（包括我们先前的工作）在未明确定义任务内涵的情况下开展工作，导致研究进展缓慢且难以应用于真实场景。为此，本文明确了音乐抄袭检测与其它MIR任务的本质差异，并厘清了需解决的核心问题。我们提出了一个名为Similar Music Pair的新数据集以支持该任务。同时，提出一种基于片段转录的方法作为解决方案。相关演示与数据集已公开于 https://github.com/Mippia/ICASSP2026-MPD。
原文摘要 · Abstract (English)
Recently, the problem of music plagiarism has emerged as an even more pressing social issue. As music information retrieval research advances, there is a growing effort to address issues related to music plagiarism. However, many studies, including our previous work, have conducted research without clearly defining what the music plagiarism detection task actually involves. This lack of a clear definition has slowed research progress and made it hard to apply results to real-world scenarios. To fix this situation, we defined how Music Plagiarism Detection is different from other MIR tasks and explained what problems need to be solved. We introduce the Similar Music Pair dataset to support this newly defined task. In addition, we propose a method based on segment transcription as one way to solve the task. Our demo and dataset are available at https://github.com/Mippia/ICASSP2026-MPD.
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