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).

翻译：本文提出了一种利用我们先前提出的结合预测与生成建模的扩散基随机再生模型进行单通道风噪声抑制的方法。我们引入了一种非加性噪声语音模型，以解释由风流引起的膜非线性变形及可能的削波现象。实验表明，在包含模拟风噪声和真实风噪声的数据集上，我们的随机再生模型优于其他基于神经网络的风噪声抑制方法，以及纯粹的预测模型和生成模型。通过在一个包含真实风噪声的未见数据集上进行测试，我们进一步证明了所提方法具有良好的泛化能力。相关音频样本、数据生成脚本及代码可在线上获取（在线链接: https://uhh.de/inf-sp-storm-wind）。