Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion on pre-trained diffusion models. The first, adopted from the image domain, allows text-based editing. The second, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples can be found on our examples page in https://hilamanor.github.io/AudioEditing/ and code can be found in https://github.com/hilamanor/AudioEditing/ .

翻译：利用大规模预训练模型以零样本方式编辑信号，近期在图像领域取得了快速进展。然而，这一浪潮尚未波及音频领域。本文探索了两种针对音频信号的零样本编辑技术，这些技术通过对预训练扩散模型应用DDPM逆推实现。第一种方法源自图像领域，支持基于文本的编辑；第二种方法则是一种新颖的无监督方式，用于发现具有语义意义的编辑方向。当应用于音乐信号时，该方法能够呈现一系列音乐层面上的有趣修改，涵盖从控制特定乐器的参与度到对旋律进行即兴改编等效果。示例音频及代码可分别访问https://hilamanor.github.io/AudioEditing/ 与 https://github.com/hilamanor/AudioEditing/ 获取。