TuneJury: An Open Metric for Improving Music Generation Preference Alignment

research work·research paper·active

Research work examining evaluation benchmark, music generation.

Verified facts

Official site	https://arxiv.org/abs/2606.17006 ↗
Geography	Global
arxiv	2606.17006
venue	32 pages, 9 figures
authors	Yonghyun Kim; Junwon Lee; Haiwen Xia; Yinghao Ma; Junghyun Koo; Koichi Saito; Yuki Mitsufuji; Chris Donahue
methods	benchmark evaluation
code urls	https://github.com/yonghyunk1m/TuneJury
demo urls	unknown
exact title	TuneJury: An Open Metric for Improving Music Generation Preference Alignment
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music generation; text-to-music generation
peer review status	not established from abstract metadata
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	Georgia Institute of Technology; Sony Group Corporation; Carnegie Mellon University
publication or preprint date	2026-06-15
abstract level neutral summary	The work studies evaluation benchmark, 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.