Hierarchical unsupervised discovery of user context from multivariate sensory data

A system capable for purely unsupervised learning of sensory context models is presented in this work. The system is based on discovery of short-term activity motifs from the sensory data and statistical analysis of these motifs on a larger time scale. Detected context segments are then clustered into high-level context categories and the data corresponding to these categories are used to train on-line classifiers for different contexts. Experiments show that the method is capable of segmenting sensory recordings into epochs of high-level environmental contexts based purely on audio signal, and that the classifiers trained from the obtained segments are selective towards specific contexts.