DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast
Text-guided audio editing aims to modify the language-specified acoustic content while preserving edit-irrelevant source components. Existing training-free methods typically rely on inversion-based editing. While inversion-free editing is appealing as it decreases computational overhead and reconstruction errors, it remains largely unexplored for audio editing. The key challenge is to construct a source-to-target editing path through diffusion denoising dynamics. In this paper, we introduce DirectAudioEdit, the first attempt to develop a training-free and inversion-free method for audio editing. Experiments on music and event-level benchmarks across two backbones show that DirectAudioEdit...
| Authors | Zhengkun Ge, Xiaoqian Liu, Haoran Zhang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.