Music Plagiarism Detection: Problem Formulation and a Segment-based Solution

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Authors: Seonghyeon Go, Yumin Kim
Cited by: 0

Abstract

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.

Fields of Study & Subjects

Music and Audio Processing Musicology and Musical Analysis Music Technology and Sound Studies

Publication details

Journal / source
arXiv (Cornell University)
Publication date
29 January 2026
Document type
Preprint