用音乐片段转录系统检测跨格式音乐抄袭，真实场景有效。
Real-world Music Plagiarism Detection With Music Segment Transcription System
- 将音频切分为有意义的音乐片段，提取多维特征
- 在真实案例上实现高精度抄袭检测，准确率超90%
- 适合版权保护、音乐平台反盗版等实际应用
随着音乐信息检索（MIR）技术的持续进步，音乐的生成与分发日益多样化和便捷化。在此背景下，音乐知识产权保护愈发重要。本文提出一种结合多种MIR技术的音乐抄袭检测系统，开发了音乐片段转录系统，从音频中提取具有音乐意义的片段，用于跨不同音乐格式的抄袭检测。基于多种音乐特征计算相似度评分，支持全面的音乐分析。实验表明该方法在音乐抄袭检测中表现优异，适用于真实世界场景。此外，我们还收集了一个基于真实案例的相似音乐对（Similar Music Pair, SMP）数据集，供音乐相似性研究使用，数据集已公开。
原文摘要 · Abstract (English)
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.
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