标题： 无监督语音修复的 unconditional 扩散模型 显示英文标题

标题： UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model

摘要： 本文介绍了UnDiff，这是一种能够解决各种语音逆向任务的扩散概率模型。经过一次无条件训练以生成语音波形后，它可以适应不同的任务，包括退化反演、神经变声以及源分离。 在本文中，我们首先通过比较不同的神经架构和预处理域来解决无条件波形生成这一具有挑战性的问题。之后，我们展示了如何利用扩散模型后训练条件技术的最新进展，将训练好的无条件扩散模型适应到语音处理的不同任务中。最后，我们在带宽扩展、去削波、变声和语音源分离等任务上展示了所提出技术的性能，并与基线进行了比较。 代码已公开可用。 显示英文摘要

摘要： This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional manner, it can be adapted to different tasks including degradation inversion, neural vocoding, and source separation. In this paper, we, first, tackle the challenging problem of unconditional waveform generation by comparing different neural architectures and preconditioning domains. After that, we demonstrate how the trained unconditional diffusion could be adapted to different tasks of speech processing by the means of recent developments in post-training conditioning of diffusion models. Finally, we demonstrate the performance of the proposed technique on the tasks of bandwidth extension, declipping, vocoding, and speech source separation and compare it to the baselines. The codes are publicly available.