MMAE: A Massive Multitask Audio Editing Benchmark

research work·benchmark study·active

Research work examining evaluation benchmark, music/audio editing.

Recorded facts

Official site	https://arxiv.org/abs/2606.07229 ↗
Geography	Global
arxiv	2606.07229
venue	Open-Source at https://github.com/ddlBoJack/MMAE
authors	Ziyang Ma; Ruiqi Yan; Ruiyang Xu; Jie Fang; Zhikang Niu; Yi-Wen Chao; Wenming Tu; Tianrui Wang; Auden; Qi Chen; Wenxi Chen; Jiaying Chi; Yanru Huo; Zixuan Jiang; Xiquan Li; Yalin Li; Junxi Liu; Minghao Liu; Binghao Qiang; Yijia Shan; Zheshu Song; Tian Tan; Zixiang Wang; Zeyu Xie; Zhifei Xie; Xiaoyu Xing; Qixiang Xu; Chen Yang; Guanrou Yang; Shan Yang; Yifan Yang; Steve Yves; Haotian Zhang; Haina Zhu; Kai Yu; Liefeng Bo; Eng-Siong Chng; Xie Chen
methods	benchmark evaluation
code urls	https://github.com/ddlBoJack/MMAE
demo urls	unknown
exact title	MMAE: A Massive Multitask Audio Editing Benchmark
project urls	unknown
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; music/audio editing
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	unknown
publication or preprint date	2026-06-05
abstract level neutral summary	The work studies evaluation benchmark, music/audio editing; 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.