DiffPhase: Generative Diffusion-based STFT Phase Retrieval

Diffusion probabilistic models have been recently used in a variety of Diffusion-based generative models are a recent innovation that tasks, including speech enhancement and synthesis. As a generative has already been shown to be very effective on various computer approach, diffusion models have been shown to be especially suitable vision tasks, including inpainting [11], super-resolution [12], text-toimage for imputation problems, where missing data is generated based on mapping [13] and more [14]. Their application is, however, not existing data. Phase retrieval is inherently an imputation problem, limited to vision tasks and they have already been used in other fields, where phase information has to be generated based on the given including text-to-speech [15], as well as speech enhancement and magnitude. In this work we build upon previous work in the speech dereverberation [16]-[19]. The basic idea is gradual addition of noise domain, adapting a speech enhancement diffusion model specifically to a clean sample until a completely corrupted sample is obtained.