Speech Signal Improvement Using Causal Generative Diffusion Models

Julius Richter; Simon Welker; Jean-Marie Lemercier; Bunlong Lay; Tal Peer; Timo Gerkmann

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arXiv · 2303.08674

Speech Signal Improvement Using Causal Generative Diffusion Models

Julius Richter·Simon Welker·Jean-Marie Lemercier·Bunlong Lay·Tal Peer·Timo Gerkmann

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Abstract

In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions. The method is based on a generative diffusion model which has been shown to work well in scenarios with missing data and non-linear corruptions. To guarantee causal processing, we modify the network architecture of our previous work and replace global normalization with causal adaptive gain control. We generate diverse training data containing a broad range of distortions. This work was performed in the context of an "ICASSP Signal Processing Grand Challenge" and submitted to the non-real-time track of the "Speech Signal Improvement Challenge 2023", where it was ranked fifth.

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Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Tal Peer, Timo Gerkmann. 2023-03-15. Speech Signal Improvement Using Causal Generative Diffusion Models. https://arxiv.org/abs/2303.08674

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