Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model

Jean-Marie Lemercier; Joachim Thiemann; Raphael Koning; Timo Gerkmann

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arXiv · 2306.12867

Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model

Jean-Marie Lemercier·Joachim Thiemann·Raphael Koning·Timo Gerkmann

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Abstract

In this paper we present a method for single-channel wind noise reduction using our previously proposed diffusion-based stochastic regeneration model combining predictive and generative modelling. We introduce a non-additive speech in noise model to account for the non-linear deformation of the membrane caused by the wind flow and possible clipping. We show that our stochastic regeneration model outperforms other neural-network-based wind noise reduction methods as well as purely predictive and generative models, on a dataset using simulated and real-recorded wind noise. We further show that the proposed method generalizes well by testing on an unseen dataset with real-recorded wind noise. Audio samples, data generation scripts and code for the proposed methods can be found online (https://uhh.de/inf-sp-storm-wind).

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BibTeX RIS

Jean-Marie Lemercier, Joachim Thiemann, Raphael Koning, Timo Gerkmann. 2023-06-22. Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model. https://arxiv.org/abs/2306.12867

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