@conference {2025,
	title = {MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection},
	booktitle = {IEEE Tencon},
	year = {2026},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Bali, Indonesia},
	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 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.},
	url = {https://arxiv.org/abs/2505.20979},
	author = {T. Lu and Geist, C-M and J. Melechovsky and A. Roy and D. Herremans}
}
@conference {2024,
	title = {MidiCaps {\textemdash} A large-scale MIDI dataset with text captions},
	booktitle = {ISMIR},
	year = {2024},
	address = {San Francisco},
	abstract = {Generative models guided by text prompts are increasingly becoming more popular. However, no text-to-MIDI models currently exist, mostly due to the lack of a captioned MIDI dataset. This work aims to enable research that combines LLMs with symbolic music by presenting the first large-scale MIDI dataset with text captions that is openly available: MidiCaps. MIDI (Musical Instrument Digital Interface) files are a widely used format for encoding musical information. Their structured format captures the nuances of musical composition and has practical applications by music producers, composers, musicologists, as well as performers. Inspired by recent advancements in captioning techniques applied to various domains, we present a large-scale curated dataset of over 168k MIDI files accompanied by textual descriptions. Each MIDI caption succinctly describes the musical content, encompassing tempo, chord progression, time signature, instruments present, genre and mood; thereby facilitating multi-modal exploration and analysis. The dataset contains a mix of various genres, styles, and complexities, offering a rich source for training and evaluating models for tasks such as music information retrieval, music understanding and cross-modal translation. We provide detailed statistics about the dataset and have assessed the quality of the captions in an extensive listening study. We anticipate that this resource will stimulate further research in the intersection of music and natural language processing, fostering advancements in both fields.},
	url = {https://arxiv.org/abs/2406.02255},
	author = {J. Melechovsky and A. Roy and D. Herremans}
}
