Loading [MathJax]/extensions/MathMenu.js
AudioEditor: A Training-Free Diffusion-Based Audio Editing Framework | IEEE Conference Publication | IEEE Xplore

AudioEditor: A Training-Free Diffusion-Based Audio Editing Framework


Abstract:

Diffusion-based text-to-audio (TTA) generation has made substantial progress, leveraging latent diffusion model (LDM) to produce high-quality, diverse and instruction-rel...Show More

Abstract:

Diffusion-based text-to-audio (TTA) generation has made substantial progress, leveraging latent diffusion model (LDM) to produce high-quality, diverse and instruction-relevant audios. However, beyond generation, the task of audio editing remains equally important but has received comparatively little attention. Audio editing tasks face two primary challenges: executing precise edits and preserving the unedited sections. While workflows based on LDMs have effectively addressed these challenges in the field of image processing, similar approaches have been scarcely applied to audio editing. In this paper, we introduce AudioEditor, a training-free audio editing framework built on the pretrained diffusion-based TTA model. AudioEditor incorporates Null-text Inversion and EOT-suppression methods, enabling the model to preserve original audio features while executing accurate edits. Comprehensive objective and subjective experiments validate the effectiveness of AudioEditor in delivering high-quality audio edits. Code and demo can be found at https://github.com/NKU-HLT/AudioEditor.
Date of Conference: 06-11 April 2025
Date Added to IEEE Xplore: 07 March 2025
ISBN Information:

ISSN Information:

Conference Location: Hyderabad, India

Contact IEEE to Subscribe

References

References is not available for this document.