细粒度音乐抄袭检测：通过二分图匹配和全面的大规模数据集揭示抄袭者
Fine-Grained Music Plagiarism Detection: Revealing Plagiarists through Bipartite Graph Matching and a Comprehensive Large-Scale Dataset
AI总结：
针对传统音乐抄袭检测粒度粗的问题，构建了大规模模拟数据集MPD-Set与真实评估集，并提出基于二分图最大匹配的BMM-Det方法，有效实现细粒度抄袭检测且对多种变体鲁棒。
摘要：
由于音乐制作的普及和社会对知识产权的重视，音乐抄袭检测正受到越来越多的关注。由于传统方法粒度较粗且无法匹配现实生活场景，我们旨在寻找音乐对中的细粒度抄袭。考虑到目前没有专为音乐抄袭任务设计的大规模数据集，我们在国家级专业机构音乐领域知名研究人员的指导和专业知识下，建立了一个大规模的模拟数据集，名为 Music Plagiarism Detection Dataset (MPD-Set)。MPD-Set 分别从旋律、节奏和音调层面考虑了现实生活中发现的各种音乐抄袭案例。此外，我们建立了一个 Real-life Dataset 用于评估，其中所有抄袭对均为真实案例。为了有效检测细粒度抄袭对，我们提出了一种基于图的方法，称为 Bipatite Melody Matching Detector (BMM-Det)，该方法将问题表述为二分图中的最大匹配问题。在模拟数据集和 Real-life Datasets 上的实验结果表明，BMM-Det 优于现有的抄袭检测方法，并且对转调、音高偏移、时值变化和旋律改变等常见抄袭案例具有鲁棒性。数据集和源代码已在 https://github.com/xuan301/BMMDet_MPDSet 开源。
英文摘要：
Music plagiarism detection is gaining more and more attention due to the popularity of music production and society's emphasis on intellectual property. We aim to find fine-grained plagiarism in music pairs since conventional methods are coarse-grained and cannot match real-life scenarios. Considering that there is no sizeable dataset designed for the music plagiarism task, we establish a large-scale simulated dataset, named Music Plagiarism Detection Dataset (MPD-Set) under the guidance and expertise of renowned researchers from national-level professional institutions in the field of music. MPD-Set considers diverse music plagiarism cases found in real life from the melodic, rhythmic, and tonal levels respectively. Further, we establish a Real-life Dataset for evaluation, where all plagiarism pairs are real cases. To detect the fine-grained plagiarism pairs effectively, we propose a graph-based method called Bipatite Melody Matching Detector (BMM-Det), which formulates the problem as a max matching problem in the bipartite graph. Experimental results on both the simulated and Real-life Datasets demonstrate that BMM-Det outperforms the existing plagiarism detection methods, and is robust to common plagiarism cases like transpositions, pitch shifts, duration variance, and melody change. Datasets and source code are open-sourced at https://github.com/xuan301/BMMDet_MPDSet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
