Nowadays, with the prevalence of social media and music creation tools, musical pieces are spreading much quickly, and music creation is getting much easier. The increasing number of musical pieces have made the problem of music plagiarism prominent. There is an urgent need for a tool that can detect music plagiarism automatically. Researchers have proposed various methods to extract low-level and high-level features of music and compute their similarities. However, low-level features such as cepstrum coefficients have weak relation with the copyright protection of musical pieces. Existing algorithms considering high-level features fail to detect the case in which two musical pieces are not quite similar overall, but have some highly similar regions. This paper proposes a new method named MESMF, which innovatively converts the music plagiarism detection problem into the bipartite graph matching task. It can be solved via the maximum weight matching and edit distances model. We design several kinds of melody representations and the similarity computation methods according to the music theory. The proposed method can deal with the shift, swapping, transposition, and tempo variance problems in music plagiarism. It can also effectively pick out the local similar regions from two musical pieces with relatively low global similarity. We collect a new music plagiarism dataset from real legally-judged music plagiarism cases and conduct detailed ablation studies. Experimental results prove the excellent performance of the proposed algorithm. The source code and our dataset are available at https://anonymous.4open.science/r/a41b8fb4-64cf-4190-a1e1-09b7499a15f5/

中文翻译：

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通过二部图匹配检测音乐剽窃

如今，随着社交媒体和音乐创作工具的流行，音乐作品的传播速度越来越快，音乐创作也变得越来越容易。音乐作品数量的不断增加，使得音乐抄袭问题日益突出。迫切需要一种可以自动检测音乐抄袭的工具。研究人员提出了各种方法来提取音乐的低级和高级特征并计算它们的相似性。然而，倒谱系数等低级特征与音乐作品的版权保护关系较弱。考虑高级特征的现有算法无法检测出两首音乐作品总体上不太相似但具有一些高度相似区域的情况。本文提出了一种名为MESMF的新方法，创新地将音乐抄袭检测问题转化为二部图匹配任务。它可以通过最大权重匹配和编辑距离模型来解决。我们根据音乐理论设计了几种旋律表示和相似度计算方法。所提出的方法可以处理音乐剽窃中的移位、交换、移调和速度变化问题。它还可以有效地从全局相似度相对较低的两首乐曲中挑选出局部相似的区域。我们从真实合法的音乐剽窃案例中收集了一个新的音乐剽窃数据集，并进行了详细的消融研究。实验结果证明了所提出算法的优异性能。源代码和我们的数据集可从 https://anonymous.4open 获得。