Online audio background determination for complex audio environments Moncrieff, Simon, Venkatesh, Svetha and West, Geoff 2007, Online audio background determination for complex audio environments, ACM transactions on multimedia computing communications and applications, vol. 3, no. 2, pp. 1-30. Attached Files Name Description MIMEType Size Downloads Title Online audio background determination for complex audio environments Author(s) Moncrieff, Simon Venkatesh, Svetha orcid.org/0000-0001-8675-6631 West, Geoff Journal name ACM transactions on multimedia computing communications and applications Volume number 3 Issue number 2 Start page 1 End page 30 Total pages 30 Publisher Association for Computing Machinery Place of publication New York, N. Y. Publication date 2007-05 ISSN 1551-6857 1551-6865 Keyword(s) audio analysis online background modelling surveillance and monitoring Summary We present a method for foreground/background separation of audio using a background modelling technique. The technique models the background in an online, unsupervised, and adaptive fashion, and is designed for application to long term surveillance and monitoring problems. The background is determined using a statistical method to model the states of the audio over time. In addition, three methods are used to increase the accuracy of background modelling in complex audio environments. Such environments can cause the failure of the statistical model to accurately capture the background states. An entropy-based approach is used to unify background representations fragmented over multiple states of the statistical model. The approach successfully unifies such background states, resulting in a more robust background model. We adaptively adjust the number of states considered background according to background complexity, resulting in the more accurate classification of background models. Finally, we use an auxiliary model cache to retain potential background states in the system. This prevents the deletion of such states due to a rapid influx of observed states that can occur for highly dynamic sections of the audio signal. The separation algorithm was successfully applied to a number of audio environments representing monitoring applications. Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture) HERDC Research category C1.1 Refereed article in a scholarly journal Copyright notice ©2007, ACM Persistent URL http://hdl.handle.net/10536/DRO/DU:30044246 Document type: Journal Article Collection: School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 05 Apr 2012, 16:00:39 EST Thu, 19 Apr 2012, 11:39:15 EST Thu, 19 Apr 2012, 11:40:48 EST Thu, 19 Apr 2012, 11:42:04 EST Tue, 24 Apr 2012, 14:56:06 EST Mon, 13 May 2013, 14:20:34 EST Fri, 13 Jun 2014, 13:41:55 EST Sat, 21 Oct 2017, 02:00:49 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 313 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 05 Apr 2012, 16:00:39 EST

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