Large audio-language models (LALMs) demonstrate growing music-understanding capabilities, but whether their responses are grounded in acoustic evidence remains unclear. Musical language often involves abstract concepts whose acoustic evidence is difficult to define and evaluate precisely. We introduce MusicGroundingBen...
Large audio-language models can produce fluent and musically plausible responses, yet it often remains unclear whether those responses are grounded in the audio input. We introduce temporal music grounding, a task in which a model returns one or more time spans corresponding to a queried musical note, event, or pattern...
A cross-modal framework that learns implicit music styles from raw audio and applies them to symbolic music generation and generates piano performances jointly conditioned on a lead sheet and a reference audio example, enabling controllable and stylistically faithful arrangement.
Jing-Wei Zhao, Gus G. Xia, Ziyu Wang et al.· 0 citations
This work proposes to learn a world model of piano sound using JEPA by framing music as an action-conditioned system, and shows that the learned model captures the relationships between musical actions and their resulting sound.
Ziyu Wang, Kun Fang, Yann LeCun· arXiv.org· 0 citations
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