Monitoring of indoors human activities using mobile phone audio recordings

Prasitthichai Naronglerdrit, Iosif Mporas, Reza Sotudeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper we present a methodology for monitoring of human activities in home using audio recordings captured from mobile phone. Specifically, after estimating a large set of audio features, unsupervised clustering is performed in order to extract feature subspaces. Human activity sound models were trained using different combinations of these subspaces. The best performance 92.46% was achieved using a neural network classifier.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages23-28
Number of pages6
ISBN (Electronic)9781509011841
DOIs
Publication statusPublished - 10 Oct 2017
Event13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017 - Penang, Malaysia
Duration: 10 Mar 201712 Mar 2017

Publication series

NameProceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017

Conference

Conference13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017
Country/TerritoryMalaysia
CityPenang
Period10/03/1712/03/17

Keywords

  • classification
  • clustering
  • human activity monitoring
  • sound recognition

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