@inproceedings{695378ca5e424b2a8e739357823d54c6,
title = "Monitoring of indoors human activities using mobile phone audio recordings",
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.",
keywords = "classification, clustering, human activity monitoring, sound recognition",
author = "Prasitthichai Naronglerdrit and Iosif Mporas and Reza Sotudeh",
year = "2017",
month = oct,
day = "10",
doi = "10.1109/CSPA.2017.8064918",
language = "English",
series = "Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "23--28",
booktitle = "Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017",
address = "United States",
note = "13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017 ; Conference date: 10-03-2017 Through 12-03-2017",
}