SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning
Authors: name: Anuradha Chopra, name: Abhinaba Roy, name: Dorien Herremans
Organization: AMAAI Lab

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.