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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.

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Title Online audio background determination for complex audio environments
Author(s) Moncrieff, Simon
Venkatesh, SvethaORCID iD for 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
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