We propose MelodySim, a melody-aware music similarity model and dataset for plagiarism detection. First, we introduce a novel method to construct a dataset focused on melodic similarity. By augmenting Slakh2100, an existing MIDI dataset, we generate variations of each piece while preserving the melody through modifications such as note splitting, arpeggiation, minor track dropout, and re-instrumentation. A user study confirms that positive pairs indeed contain similar melodies, while other musical tracks are significantly changed. Second, we develop a segment-wise melodic-similarity detection model that uses a MERT encoder and applies a triplet neural network to capture melodic similarity. The resulting decision matrix highlights where plagiarism might occur. The experiments show that our model is able to outperform baseline models in detecting similar melodic fragments on the MelodySim test set.

翻译：我们提出了MelodySim，一种用于抄袭检测的旋律感知音乐相似性模型及数据集。首先，我们引入了一种新颖的方法来构建专注于旋律相似性的数据集。通过对现有MIDI数据集Slakh2100进行增强，我们生成了每首乐曲的变体，同时通过音符分割、琶音化、次要音轨丢弃和重新配器等修改手段保留旋律。一项用户研究证实，正样本对确实包含相似的旋律，而其他音乐轨道则发生了显著变化。其次，我们开发了一种分段式旋律相似性检测模型，该模型使用MERT编码器，并应用三元组神经网络来捕捉旋律相似性。生成的决策矩阵突出了可能发生抄袭的位置。实验表明，我们的模型在MelodySim测试集上检测相似旋律片段的能力优于基线模型。