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

Conference Paper


Abstract


  • 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.

UOW Authors


  •   Phillips, Lincoln (external author)
  •   Porter, Christopher B. (external author)
  •   Kottege, Navinda (external author)
  •   D'Souza, Matthew (external author)
  •   Ros, Montserrat

Publication Date


  • 2015

Citation


  • L. Phillips, C. Berry. Porter, N. Kottege, M. D'Souza & M. Ros, "Machine learning based acoustic sensing for indoor room localisation using mobile phones," in Proceedings of the International Conference on Sensing Technology, ICST, 2015, pp. 456-460.

Scopus Eid


  • 2-s2.0-84964855292

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5526

Start Page


  • 456

End Page


  • 460

Place Of Publication


  • United States

Abstract


  • 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.

UOW Authors


  •   Phillips, Lincoln (external author)
  •   Porter, Christopher B. (external author)
  •   Kottege, Navinda (external author)
  •   D'Souza, Matthew (external author)
  •   Ros, Montserrat

Publication Date


  • 2015

Citation


  • L. Phillips, C. Berry. Porter, N. Kottege, M. D'Souza & M. Ros, "Machine learning based acoustic sensing for indoor room localisation using mobile phones," in Proceedings of the International Conference on Sensing Technology, ICST, 2015, pp. 456-460.

Scopus Eid


  • 2-s2.0-84964855292

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5526

Start Page


  • 456

End Page


  • 460

Place Of Publication


  • United States