@conference {2025,
	title = {SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning},
	booktitle = {Proceedings of the 6th Conference on AI Music Creativity (AIMC 2025), Brussels, Belgium, September 10th - 12th, 2025},
	year = {2025},
	abstract = {Detailed captions that accurately reflect the characteristics of a music piece can enrich music databases and drive forward research in music AI. This paper introduces a multi-task music captioning model, SonicVerse, that integrates caption generation with auxiliary music feature detection tasks such as key detection, vocals detection, and more, so as to directly capture both low-level acoustic details as well as high-level musical attributes. The key contribution is a projection-based architecture that transforms audio input into language tokens, while simultaneously detecting music features through dedicated auxiliary heads. The outputs of these heads are also projected into language tokens, to enhance the captioning input. This framework not only produces rich, descriptive captions for short music fragments but also directly enables the generation of detailed time-informed descriptions for longer music pieces, by chaining the outputs using a large-language model. To train the model, we extended the MusicBench dataset by annotating it with music features using MIRFLEX, a modular music feature extractor, resulting in paired audio, captions and music feature data. Experimental results show that incorporating features in this way improves the quality and detail of the generated captions.},
	keywords = {captioning, music, nlp},
	url = {https://arxiv.org/abs/2506.15154},
	author = {A. Chopra and A. Roy and D. Herremans}
}
@article {132,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	year = {2018},
	keywords = {music, word2vec},
	doi = {10.1007/s00521-018-3923-1},
	url = {http://link.springer.com/article/10.1007/s00521-018-3923-1},
	author = {C.-H. Chuan and K. Agres and D. Herremans}
}
@proceedings {84,
	title = {Modeling Musical Context with Word2vec},
	volume = {1},
	year = {2017},
	month = {05/2017},
	pages = {11-18},
	address = {Anchorage, US},
	abstract = {We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven{\textquoteright}s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows
that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.},
	keywords = {music, music context, neural networks, semantic vector space, word2vec},
	doi = {10.13140/RG.2.2.22227.99364/1},
	url = {dorienherremans.com/dlm2017},
	author = {D. Herremans and C.-H. Chuan}
}
