Prompt-guided Precise Audio Editing with Diffusion Models

• URL: http://arxiv.org/abs/2406.04350v1
• Date: Sat, 11 May 2024 07:41:27 GMT
• Title: Prompt-guided Precise Audio Editing with Diffusion Models
• Authors: Manjie Xu, Chenxing Li, Duzhen zhang, Dan Su, Wei Liang, Dong Yu,
• Abstract summary: PPAE serves as a general module for diffusion models and enables precise audio editing. We exploit the cross-attention maps of diffusion models to facilitate accurate local editing and employ a hierarchical local-global pipeline to ensure a smoother editing process.
• Score: 36.29823730882074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Audio editing involves the arbitrary manipulation of audio content through precise control. Although text-guided diffusion models have made significant advancements in text-to-audio generation, they still face challenges in finding a flexible and precise way to modify target events within an audio track. We present a novel approach, referred to as PPAE, which serves as a general module for diffusion models and enables precise audio editing. The editing is based on the input textual prompt only and is entirely training-free. We exploit the cross-attention maps of diffusion models to facilitate accurate local editing and employ a hierarchical local-global pipeline to ensure a smoother editing process. Experimental results highlight the effectiveness of our method in various editing tasks.

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