MEMS Accelerometer based Acoustic Real-time Event Sensors
Abstract
A new system, the Acoustic Real-time Event Sensors (ARES), has been developed for autonomously monitoring the directional sound field in urban, transportation and industrial settings, managed within an IoT network. The directional information provided by each sensor enables a fully unattended sparse acoustic monitoring network that can discriminate specific sources of sound from overall noise levels at a measurement location, even if that noise is very close in proximity. While possible to make a network with existing 3D microphone-based acoustic intensity or camera solutions, these options are unwieldy and expensive for real-time deployments. The ARES sensor takes a different approach, measuring the velocity of a small parcel of air surrounding a triaxial accelerometer, from which a vector-based representation of sound intensity is calculated. Each ARES sensor node is compact directional sensor, sensitive to low frequencies with acoustic wavelengths much greater the ARES sensor). Sensitivity to the acoustic field is enabled by using a lightweight MEMS accelerometer paired with a MEMS microphone, both housed within an ultra-lightweight polystyrene sphere. Calibrations are applied to create gap free short time-spectral data stream, which are sampled by a small low power Raspberry Pi(RPi) computer that can manage the data from multiple nodes through a wired CAN bus supporting distances up to 100 m.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 18, 2021
- Accession Number
- AD1216147
Entities
People
- David Dall'osto
Organizations
- University of Washington