Fast Timing-Conditioned Latent Audio Diffusion

research work·research paper·active

Research work examining evaluation benchmark, text-to-music generation.

Recorded facts

Official site	https://arxiv.org/abs/2402.04825 ↗
Geography	Global
arxiv	2402.04825
venue	Accepted to ICML 2024. Code: https://github.com/Stability-AI/stable-audio-tools. Metrics: https://github.com/Stability-AI/stable-audio-metrics. Demo: https://stability-ai.github.io/stable-audio-demo
authors	Zach Evans; CJ Carr; Josiah Taylor; Scott H. Hawley; Jordi Pons
methods	benchmark evaluation; diffusion modeling
code urls	https://github.com/Stability-AI/stable-audio-metrics; https://github.com/Stability-AI/stable-audio-tools
demo urls	https://github.com/Stability-AI/stable-audio-metrics; https://github.com/Stability-AI/stable-audio-tools; https://stability-ai.github.io/stable-audio-demo
exact title	Fast Timing-Conditioned Latent Audio Diffusion
project urls	https://stability-ai.github.io/stable-audio-demo
original title	unknown
citation counts	unknown
research topics	evaluation benchmark; 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	Stability AI; Harmonai
publication or preprint date	2024-02-07
abstract level neutral summary	The work studies evaluation benchmark, text-to-music generation; methods and evaluation details are in the official abstract.
datasets benchmarks models tools used	Stable Audio
correction withdrawal retraction status	No withdrawal marker observed in captured arXiv metadata.