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.
@article{arxiv.2303.08674,
title = {Speech Signal Improvement Using Causal Generative Diffusion Models},
author = {Julius Richter and Simon Welker and Jean-Marie Lemercier and Bunlong Lay and Tal Peer and Timo Gerkmann},
journal= {arXiv preprint arXiv:2303.08674},
year = {2023}
}