In this paper we extend an existing audio background modelling technique, leading to a more robust application to complex audio environments. The determination of background audio is used as an initial stage in the analysis of audio for surveillance and monitoring applications. Knowledge of the background serves to highlight unusual or infrequent sounds. An existing modelling approach uses an online, adaptive Gaussian Mixture model technique that uses multiple distributions to model variations in the background. The method used to determine the background distributions of the GMM leads to a failure mode of the existing technique when applied to complex audio. We propose a method incorporating further information, the proximity of distributions determined using entropy, to determine a more complete background model. The method was successful in more robustly modelling the background for complex audio scenes.
History
Event
International Conference on Pattern Recognition (18th : 2006 : Hong Kong, China)
Pagination
249 - 253
Publisher
IEEE
Location
Hong Kong, China
Place of publication
Washington, D. C.
Start date
2006-08-20
End date
2006-08-24
ISSN
1051-4651
ISBN-10
0769525210
Language
eng
Notes
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Publication classification
E1.1 Full written paper - refereed
Copyright notice
2006, IEEE
Title of proceedings
ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition