DocumentCode :
3152570
Title :
Hierarchical unsupervised discovery of user context from multivariate sensory data
Author :
Räsänen, Okko
Author_Institution :
Dept. of Signal Process. & Acoust., Aalto Univ., Aalto, Finland
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
2105
Lastpage :
2108
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;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2012.6288326
Filename :
6288326
Link To Document :
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