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Unifying background models over complex audio using entropy

Moncrieff, Simon, Venkatesh, Svetha and West, Geoff 2006, Unifying background models over complex audio using entropy, in ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 249-253.

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Title Unifying background models over complex audio using entropy
Author(s) Moncrieff, Simon
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff
Conference name International Conference on Pattern Recognition (18th : 2006 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 20-24 Aug. 2006
Title of proceedings ICPR 2006 : Proceedings of the 18th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2006
Conference series International Conference on Pattern Recognition
Start page 249
End page 253
Total pages 5
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) adaptive gaussian mixture models
complex audio environments
model variations
Summary 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.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769525210
ISSN 1051-4651
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2006, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044605

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.