Music Plagiarism Detection: Problem Formulation and a Segment-based Solution
This work addresses the problem of ambiguous task definitions in music information retrieval for researchers and practitioners, though it is incremental in nature.
The paper tackles the lack of a clear definition for music plagiarism detection by formulating the task and proposing a segment-based solution, introducing the Similar Music Pair dataset to support it.
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.