Visible to the public Fingerprinting Method for Acoustic Localization Using Low-Profile Microphone Arrays

TitleFingerprinting Method for Acoustic Localization Using Low-Profile Microphone Arrays
Publication TypeConference Paper
Year of Publication2018
AuthorsThoen, B., Wielandt, S., Strycker, L. De
Conference Name2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
Date PublishedSept. 2018
ISBN Number978-1-5386-5635-8
Keywordsacoustic event localization, Acoustic Fingerprinting, Acoustic Fingerprints, Acoustic localization, acoustic measurement, Acoustic measurements, acoustic sensor nodes, Acoustic signal processing, acoustic transducers, Acoustics, anechoic room, Angle of Arriva fingerprinting method, AoA fingerprinting algorithm, composability, computationally efficient time domain delay-based AoA algorithms, Correlation, direction-of-arrival estimation, dot product calculations, female vocal sounds, frequency-domain analysis, Human Behavior, indoor localization, low-profile microphone arrays, mean localization errors, mean square error methods, MEMS microphone arrays, microphone array, microphone arrays, microphones, PHAse Transform, Probability density function, pubcrawl, research phase, Resiliency, Root Mean Square Errors, sound events, unknown acoustic events, White noise, Wireless Acoustic Sensor Networks, Wireless sensor networks

Indoor localization of unknown acoustic events with MEMS microphone arrays have a huge potential in applications like home assisted living and surveillance. This article presents an Angle of Arrival (AoA) fingerprinting method for use in Wireless Acoustic Sensor Networks (WASNs) with low-profile microphone arrays. In a first research phase, acoustic measurements are performed in an anechoic room to evaluate two computationally efficient time domain delay-based AoA algorithms: one based on dot product calculations and another based on dot products with a PHAse Transform (PHAT). The evaluation of the algorithms is conducted with two sound events: white noise and a female voice. The algorithms are able to calculate the AoA with Root Mean Square Errors (RMSEs) of 3.5deg for white noise and 9.8deg to 16deg for female vocal sounds. In the second research phase, an AoA fingerprinting algorithm is developed for acoustic event localization. The proposed solution is experimentally verified in a room of 4.25 m by 9.20 m with 4 acoustic sensor nodes. Acoustic fingerprints of white noise, recorded along a predefined grid in the room, are used to localize white noise and vocal sounds. The localization errors are evaluated using one node at a time, resulting in mean localization errors between 0.65 m and 0.98 m for white noise and between 1.18 m and 1.52 m for vocal sounds.

Citation Keythoen_fingerprinting_2018