Real-world Music Plagiarism Detection With Music Segment Transcription System
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Abstract (arXiv)
As a result of continuous advances in Music Information Retrieval (MIR) technology, generating and distributing music has become more diverse and accessible. In this context, interest in music intellectual property protection is increasing to safeguard individual music copyrights. In this work, we propose a system for detecting music plagiarism by combining various MIR technologies. We developed a music segment transcription system that extracts musically meaningful segments from audio recordings to detect plagiarism across different musical formats. With this system, we compute similarity scores based on multiple musical features that can be evaluated through comprehensive musical analysis. Our approach demonstrated promising results in music plagiarism detection experiments, and the proposed method can be applied to real-world music scenarios. We also collected a Similar Music Pair (SMP) dataset for musical similarity research using real-world cases. The dataset are publicly available.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 10 Sep 2025.
Builds on
12 of 29 references.
References · 12
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- 2022Hybrid Transformers for Music Source Separationcited by 134
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- 2022Melody transcription via generative pre-trainingcited by 24
- 2022MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learningcited by 22
- 2019A holistic approach to polyphonic music transcription with neural networkscited by 11
- 2022Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attentioncited by 10
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- 2023CoverHunter: Cover Song Identification with Refined Attention and Alignmentscited by 6
- 2015Plagiarism Detection in Polyphonic Music using Monaural Signal Separationcited by 2
- 2024Just Label the Repeats for In-The-Wild Audio-to-Score Alignmentcited by 2
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Citing papers per year: 2026: 1.
1 paper.