Autonomous monitoring of traffic, rail, and industrial noise using acoustic vector beamformers based on 3D MEMS accelerometers

Abstract

Fully unattended acoustic monitoring has been hindered by the need to automatically discriminate specific sources from overall noise levels at a measurement location, even if that noise is very close in proximity. Existing 3D microphone-based acoustic intensity or camera solutions are unwieldy and expensive for real-time deployments. A new system has been developed for autonomously monitoring noise levels in transportation and industrial settings, managed within an IoT network. Each measurement node has one or two Acoustic Real-time Event Sensors (ARES) and a beamformer algorithm using 3D MEMS accelerometer-enabled Acoustic Vector Sensing (AVS) technology. Beamforming enables the system to focus on specific areas or sources of noise, delivering more precise monitoring and identification of noise sources, useful for noise reduction efforts and compliance with noise regulations. Deploying 3D accelerometers, rather than microphone arrays, in the beamformer provides improved system performance and environmental protection, with reductions in array size, cost, and unwanted sidelobes. ARES beamformer array apertures occupy just 1 cm for a single sensor, or 13 cm for 2 sensors, and can distinguish sources in frequencies from 50 to 2 kHz with excellent angular resolution, in real-time. Example traffic and rail noise applications are presented.