@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 {2025,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Proceedings of ICASSP},
	year = {2025},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec. We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	url = {https://openreview.net/pdf/b3dcd6d5d6c26679621a2e6c176455d59658c0a8.pdf},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@conference {2025,
	title = {ImprovNet: Generating Controllable Musical Improvisations with Iterative Corruption Refinement},
	booktitle = {Proceedings of IJCNN},
	year = {2025},
	address = {Rome, Italy},
	abstract = {Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model{\textquoteright}s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet{\textquoteright}s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79\% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at this https URL (https://github.com/keshavbhandari/improvnet).},
	doi = {10.1109/IJCNN64981.2025.11229312},
	url = {https://arxiv.org/abs/2502.04522},
	author = {K. Bhandari and S. Chang and T. Lu and F. R. Enus and L. B. Bradshaw and D. Herremans and S. Colton}
}
@conference {2025,
	title = {JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	abstract = {We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 200,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models.},
	doi = {10.1109/IJCNN64981.2025.11228034},
	url = {https://arxiv.org/abs/2502.07461},
	author = {A. Roy and R. Liu and T. Lu and D. Herremans}
}
@conference {2024,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Audio Imagination: NeurIPS 2024 Workshop},
	year = {2024},
	address = {Vancouver},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec.  We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
