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arXiv · 2507.01821

Low-Complexity Neural Wind Noise Reduction for Audio Recordings

Hesam Eftekhari·Srikanth Raj Chetupalli·Shrishti Saha Shetu·Emanuël A. P. Habets·Oliver Thiergart
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Abstract

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.

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Hesam Eftekhari, Srikanth Raj Chetupalli, Shrishti Saha Shetu, Emanuël A. P. Habets, Oliver Thiergart. 2025-07-02. Low-Complexity Neural Wind Noise Reduction for Audio Recordings. https://arxiv.org/abs/2507.01821

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