Low-Complexity Neural Wind Noise Reduction for Audio Recordings

TL;DR

This paper introduces a low-complexity deep neural network designed to effectively reduce wind noise in outdoor audio recordings, suitable for real-time use on resource-limited devices.

Contribution

The authors develop a lightweight neural network with 249K parameters that matches state-of-the-art performance while being computationally efficient for embedded systems.

Findings

Achieves comparable performance to existing low-complexity models

Uses only 73 MHz of computational power

Suitable for real-time embedded audio applications

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.