Low-Complexity Neural Wind Noise Reduction for Audio Recordings

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{eftekhari2025_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 }
}