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

TL;DR

This paper clearly defines the music plagiarism detection problem, introduces a new dataset, and proposes a segment-based method to improve detection accuracy in real-world applications.

Contribution

It provides a formal definition of music plagiarism detection, introduces the Similar Music Pair dataset, and proposes a segment transcription-based solution.

Findings

Introduction of the Similar Music Pair dataset

Proposed segment transcription method for detection

Clarification of the task's scope and challenges

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.