Title :
Hierarchical unsupervised discovery of user context from multivariate sensory data
Author_Institution :
Dept. of Signal Process. & Acoust., Aalto Univ., Aalto, Finland
Abstract :
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.
Keywords :
audio signal processing; statistical analysis; unsupervised learning; audio signal; hierarchical unsupervised discovery; high-level context categories; high-level environmental contexts; multivariate sensory data; sensory recordings; statistical analysis; unsupervised learning; user context; Computational modeling; Context; Feature extraction; Hidden Markov models; Sensors; Speech; Unsupervised learning; context recognition; machine learning; unsupervised learning;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2012.6288326