DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast
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Computer Science > Sound
Title:DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast
Abstract: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 reduces macro-averaged FAD and KL by 15.9% and 15.8% compared with DDPM inversion, while achieving up to 64.5% editing speedup.
| Subjects: | Sound (cs.SD); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.07356 [cs.SD] |
| (or arXiv:2606.07356v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07356
arXiv-issued DOI via DataCite (pending registration)
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