# Real-world Music Plagiarism Detection With Music Segment Transcription System

arXiv: https://arxiv.org/abs/2509.08282 · HTML: https://mlsanity.com/paper/2509.08282/

## Key facts

- Cited by 1 papers 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 (most cited first, 12 of 12)

- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [All-In-One Metrical And Functional Structure Analysis With Neighborhood Attentions on Demixed Audio](https://mlsanity.com/paper/2307.16425/) (2023)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning](https://mlsanity.com/paper/2212.02508/) (2022)
- [A holistic approach to polyphonic music transcription with neural networks](https://mlsanity.com/paper/1910.12086/) (2019)
- [Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention](https://mlsanity.com/paper/2209.07140/) (2022)
- [Learning a Representation for Cover Song Identification Using Convolutional Neural Network](https://mlsanity.com/paper/1911.00334/) (2019)
- [ByteCover3: Accurate Cover Song Identification on Short Queries](https://mlsanity.com/paper/2303.11692/) (2023)
- [CoverHunter: Cover Song Identification with Refined Attention and Alignments](https://mlsanity.com/paper/2306.09025/) (2023)
- [Plagiarism Detection in Polyphonic Music using Monaural Signal Separation](https://mlsanity.com/paper/1503.00022/) (2015)
- [Just Label the Repeats for In-The-Wild Audio-to-Score Alignment](https://mlsanity.com/paper/2411.07428/) (2024)

## Built on by (most cited first, 1 of 1)

- [Music Plagiarism Detection: Problem Formulation and a Segment-based Solution](https://mlsanity.com/paper/2601.21260/) (2026)

## 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.