Wind Noise Reduction

Wind noise reduction aims to improve the quality and intelligibility of audio recordings made in windy conditions, a significant challenge due to wind's non-stationary and often non-linear effects on sound. Current research focuses on advanced deep learning architectures, including convolutional and capsule neural networks, and explores multimodal approaches that integrate data from acoustic microphones with auxiliary sensors like ultrasound devices or contact microphones to better characterize and mitigate wind noise. These improvements have significant implications for various applications, such as mobile communication and speech recognition systems, by enhancing robustness and performance in real-world noisy environments.