DiffPhase: Generative Diffusion-based STFT Phase Retrieval

• URL: http://arxiv.org/abs/2211.04332v2
• Date: Fri, 2 Jun 2023 08:50:05 GMT
• Title: DiffPhase: Generative Diffusion-based STFT Phase Retrieval
• Authors: Tal Peer, Simon Welker, Timo Gerkmann
• Abstract summary: Diffusion probabilistic models have been recently used in a variety of tasks, including speech enhancement and synthesis. In this work we build upon previous work in the speech domain, adapting a speech enhancement diffusion model specifically for phase retrieval. Evaluation using speech quality and intelligibility metrics shows the diffusion approach is well-suited to the phase retrieval task, with performance surpassing both classical and modern methods.
• Score: 15.16865739526702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion probabilistic models have been recently used in a variety of tasks, including speech enhancement and synthesis. As a generative approach, diffusion models have been shown to be especially suitable for imputation problems, where missing data is generated based on existing data. Phase retrieval is inherently an imputation problem, where phase information has to be generated based on the given magnitude. In this work we build upon previous work in the speech domain, adapting a speech enhancement diffusion model specifically for STFT phase retrieval. Evaluation using speech quality and intelligibility metrics shows the diffusion approach is well-suited to the phase retrieval task, with performance surpassing both classical and modern methods.

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