1 research outputs found
Real-Time Wind Noise Detection and Suppression with Neural-Based Signal Reconstruction for Mult-Channel, Low-Power Devices
Active wind noise detection and suppression techniques are a new and
essential paradigm for enhancing ASR-based functionality with smart glasses, in
addition to other wearable and smart devices in the broader IoT (Internet of
things). In this paper, we develop two separate algorithms for wind noise
detection and suppression, respectively, operational in a challenging,
low-energy regime. Together, these algorithms comprise a robust wind noise
suppression system. In the first case, we advance a real-time wind detection
algorithm (RTWD) that uses two distinct sets of low-dimensional signal features
to discriminate the presence of wind noise with high accuracy. For wind noise
suppression, we employ an additional algorithm - attentive neural wind
suppression (ANWS) - that utilizes a neural network to reconstruct the wearer
speech signal from wind-corrupted audio in the spectral regions that are most
adversely affected by wind noise. Finally, we test our algorithms through
real-time experiments using low-power, multi-microphone devices with a wind
simulator under challenging detection criteria and a variety of wind
intensities.Comment: 5 pages, 8 figure