@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}
}
@conference {2024,
	title = {MIRFLEX: Music Information Retrieval Feature Library for Extraction},
	booktitle = {ISMIR, Late Breaking Demos},
	year = {2024},
	address = {San Francisco},
	abstract = {This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genre, as well as audio characteristics like instrument recognition, vocals/instrumental classification, and vocals gender detection. The integrated models are state-of-the-art or latest open-source. The features can be extracted as latent or post-processed labels, enabling integration into music applications such as generative music, recommendation, and playlist generation. The modular design allows easy integration of newly developed systems, making it a good benchmarking and comparison tool. This versatile toolkit supports the research community in developing innovative solutions by providing concrete musical features.
},
	url = {https://arxiv.org/abs/2411.00469},
	author = {A. Chopra and A. Roy and D. Herremans}
}
