DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment

research work·research paper·active

Research work examining text-to-music generation.

Recorded facts

Official site	https://arxiv.org/abs/2606.10010 ↗
Geography	Global
arxiv	2606.10010
venue	Accepted to IEEE Signal Processing Letters (SPL)
authors	Chien-Chun Wang; Hung-Shin Lee; Hsin-Min Wang; Berlin Chen
methods	unknown
code urls	unknown
demo urls	unknown
exact title	DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
project urls	unknown
original title	unknown
citation counts	unknown
research topics	text-to-music generation
peer review status	venue or acceptance claim recorded in arXiv comment
disclosed conflicts	unknown
stated contribution	Presents or evaluates the system, method, benchmark, or analysis identified in the paper title.
us market scope basis	Included as a materially relevant public research artifact in the US-facing AI-music ecosystem; direct affiliation varies.
funding acknowledgements	unknown
affiliations at publication	unknown
publication or preprint date	2026-06-08
abstract level neutral summary	The work studies text-to-music generation; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	unknown
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.