Monitoring of indoors human activities using mobile phone audio recordings

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.