{"ID":2899267,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01821","arxiv_id":"2507.01821","title":"Low-Complexity Neural Wind Noise Reduction for Audio Recordings","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.","short_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 tha...","url_abs":"https://arxiv.org/abs/2507.01821","url_pdf":"https://arxiv.org/pdf/2507.01821v1","authors":"[\"Hesam Eftekhari\",\"Srikanth Raj Chetupalli\",\"Shrishti Saha Shetu\",\"Emanuël A. P. Habets\",\"Oliver Thiergart\"]","published":"2025-07-02T15:36:54Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.SD\",\"eess.SP\"]","methods":"[]","has_code":false}