基于扩散模型的提示引导精确音频编辑
Prompt-guided Precise Audio Editing with Diffusion Models
AI总结：
本文提出无需训练的通用模块PPAE，利用扩散模型交叉注意力图和分层局部-全局流水线，仅通过文本提示实现精确音频编辑。
摘要：
音频编辑涉及通过精确控制对音频内容进行任意操作。尽管文本引导的扩散模型在文本到音频生成方面取得了显著进展，但它们在寻找灵活且精确的方式来修改音频轨道中的目标事件方面仍面临挑战。我们提出了一种新颖的方法，称为PPAE，它作为扩散模型的通用模块，能够实现精确的音频编辑。该编辑仅基于输入的文本提示，并且完全无需训练。我们利用扩散模型的交叉注意力图来促进准确的局部编辑，并采用分层局部-全局流水线以确保更平滑的编辑过程。实验结果突显了我们的方法在各种编辑任务中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
