MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection
This addresses plagiarism detection for music creators and analysts, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of detecting plagiarism in music by developing MelodySim, a melody-aware similarity model and dataset, which outperformed baseline models in identifying similar melodic fragments.
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.