Machine learning based acoustic sensing for indoor room localisation using mobile phones

Phillips, Lincoln, Berry Porter, Christopher, Kottege, Navinda, D'Souza, Matthew and Ros, Montserrat (2015). Machine learning based acoustic sensing for indoor room localisation using mobile phones. In: 2015 9th International Conference on Sensing Technology (ICST). International Conference on Sensing Technology (ICST), Auckland, New Zealand, (456-460). 8-10 December 2015. doi:10.1109/ICSensT.2015.7438442


Author Phillips, Lincoln
Berry Porter, Christopher
Kottege, Navinda
D'Souza, Matthew
Ros, Montserrat
Title of paper Machine learning based acoustic sensing for indoor room localisation using mobile phones
Conference name International Conference on Sensing Technology (ICST)
Conference location Auckland, New Zealand
Conference dates 8-10 December 2015
Convener IEEE
Proceedings title 2015 9th International Conference on Sensing Technology (ICST)
Journal name Proceedings of the International Conference on Sensing Technology, ICST
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/ICSensT.2015.7438442
Open Access Status Not Open Access
ISBN 9781479963140
ISSN 2156-8073
Volume 2016-March
Start page 456
End page 460
Total pages 5
Collection year 2016
Language eng
Abstract/Summary We present a novel indoor localisation system that used acoustic sensing. We developed the Acoustic Landmark Locator to determine a person's current room location, within a building. Indoor environments tend to have distinct acoustic properties due to physical structure. Hence rooms in a building can have distinctive acoustic signatures. We found that these acoustic signatures can determine the position of a person. We attempted to identify location based on acoustic sensing of the surrounding indoor environment. We developed a mobile phone application that determined a person's location by measuring the acoustic levels of the surrounding environment. We used a machine learning artificial neural network based algorithm to classify the location of the person, within proximity to a landmark or room. We tested the Acoustic Landmark Locator in an indoor environment. Our tests show that the Acoustic Landmark Locator mobile phone app was able to successfully determine the location of the person carrying the mobile phone, in all test areas. It was also found that background noise caused by the presence of people does distort the landmark acoustic profiles but the artificial neural network based classifier was able to reliably determine the person's room location. Further work will involve investigating how other machine learning approaches can be used to better improve position accuracy.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Thu, 05 May 2016, 01:52:09 EST by Anthony Yeates on behalf of Learning and Research Services (UQ Library)