Low-Complexity Neural Wind Noise Reduction for Audio Recordings

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

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.

External IDs:dblp:journals/corr/abs-2507-01821