WavJourney: Compositional Audio Creation with Large Language Models

research work·research paper·active

Research work examining audio generation, evaluation benchmark.

Recorded facts

Official site	https://arxiv.org/abs/2307.14335 ↗
Geography	Global
arxiv	2307.14335
venue	GitHub: https://github.com/Audio-AGI/WavJourney
authors	Xubo Liu; Zhongkai Zhu; Haohe Liu; Yi Yuan; Meng Cui; Qiushi Huang; Jinhua Liang; Yin Cao; Qiuqiang Kong; Mark D. Plumbley; Wenwu Wang
methods	benchmark evaluation; language modeling
code urls	https://github.com/Audio-AGI/WavJourney
demo urls	https://audio-agi.github.io/WavJourney_demopage/; https://github.com/Audio-AGI/WavJourney
exact title	WavJourney: Compositional Audio Creation with Large Language Models
project urls	https://audio-agi.github.io/WavJourney_demopage/
original title	unknown
citation counts	unknown
research topics	audio generation; evaluation benchmark; music transcription; music/audio representation learning; text-to-audio 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	unknown
publication or preprint date	2023-07-26
abstract level neutral summary	The work studies audio generation, evaluation benchmark; 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.