MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection

• URL: http://arxiv.org/abs/2505.20979v1
• Date: Tue, 27 May 2025 10:14:03 GMT
• Title: MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection
• Authors: Tongyu Lu, Charlotta-Marlena Geist, Jan Melechovsky, Abhinaba Roy, Dorien Herremans,
• Abstract summary: MelodySim is a melody-aware music similarity model and dataset for plagiarism detection.<n>By augmenting Slakh2100; an existing MIDI dataset, we generate variations of each piece while preserving the melody.<n>A user study confirms that positive pairs indeed contain similar melodies, with other musical tracks significantly changed.
• Score: 5.80717487254309
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose MelodySim, a melody-aware music similarity model and dataset for plagiarism detection. First, we introduce a novel method to construct a dataset with focus 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 (excluding bass), and re-instrumentation. A user study confirms that positive pairs indeed contain similar melodies, with other musical tracks 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 resultant decision matrix highlights where plagiarism might occur. Our model achieves high accuracy on the MelodySim test set.

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