1 citations · 2 across the 2 of their papers we have counts for
3 papers
eess.AS2024
High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching
Gael Le Lan, Bowen Shi, Zhaoheng Ni +9
We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48…
cs.SD2023★ 1 cited
On The Open Prompt Challenge In Conditional Audio Generation
Ernie Chang, Sidd Srinivasan, Mahi Luthra +8
Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text. However, commercializing audio generation is ch…
cs.SD2023★ 1 cited
In-Context Prompt Editing For Conditional Audio Generation
Ernie Chang, Pin-Jie Lin, Yang Li +6
Distributional shift is a central challenge in the deployment of machine learning models as they can be ill-equipped for real-world data. This is particularly evident in text-to-au…