Wind noise significantly degrades the quality of outdoor audio recordings, yet remains difficult to suppress in real-time on resource-constrained devices. In this work, we propose a low-complexity single-channel deep neural network that leverages the spectral characteristics of wind noise. Experimental results show that our method achieves performance comparable to the state-of-the-art low-complexity ULCNet model. The proposed model, with only 249K parameters and roughly 73 MHz of computational power, is suitable for embedded and mobile audio applications.
@article{arxiv.2507.01821,
title = {Low-Complexity Neural Wind Noise Reduction for Audio Recordings},
author = {Hesam Eftekhari and Srikanth Raj Chetupalli and Shrishti Saha Shetu and Emanuël A. P. Habets and Oliver Thiergart},
journal= {arXiv preprint arXiv:2507.01821},
year = {2025}
}