Prompt-guided Precise Audio Editing with Diffusion Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910475392909312 |
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| author | Xu, Manjie Li, Chenxing zhang, Duzhen Su, Dan Liang, Wei Yu, Dong |
| author_facet | Xu, Manjie Li, Chenxing zhang, Duzhen Su, Dan Liang, Wei Yu, Dong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04350 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt-guided Precise Audio Editing with Diffusion Models Xu, Manjie Li, Chenxing zhang, Duzhen Su, Dan Liang, Wei Yu, Dong Sound Artificial Intelligence Machine Learning Audio and Speech Processing 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. |
| title | Prompt-guided Precise Audio Editing with Diffusion Models |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.04350 |