Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914634681810944 |
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| author | Lemercier, Jean-Marie Thiemann, Joachim Koning, Raphael Gerkmann, Timo |
| author_facet | Lemercier, Jean-Marie Thiemann, Joachim Koning, Raphael Gerkmann, Timo |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12867 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model Lemercier, Jean-Marie Thiemann, Joachim Koning, Raphael Gerkmann, Timo Audio and Speech Processing Machine Learning Sound 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). |
| title | Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2306.12867 |