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    Unifying background models over complex audio using entropy

    116826_Unifying%20background%20models%20PID%20116826.pdf (185.7Kb)
    Access Status
    Open access
    Authors
    Moncrieff, Simon
    Venkatesh, Svetha
    West, Geoffrey
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Moncrieff, Simon and Venkatesh, Svetha and West, Geoffrey. 2006. Unifying background models over complex audio using entropy, in Tang, Y. and Wang, S. and Lorette, G. and Young, D. and Yang, H. (ed), 18th International Conference on Pattern Recognition 'ICPR', Aug 20 2006, Vol. 4: pp. 249-253. Hong Kong: IEEE Computer Society.
    Source Title
    Proceedings of the 18th International Conference on Pattern Recognition Vol 4
    Source Conference
    International Conference on Pattern Recognition 2006
    DOI
    10.1109/ICPR.2006.1141
    ISBN
    0769525210
    ISSN
    10514651
    Faculty
    School of Electrical Engineering and Computing
    Department of Computing
    Faculty of Science and Engineering
    Remarks

    Copyright © 2006 IEEE. 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.

    URI
    http://hdl.handle.net/20.500.11937/42503
    Collection
    • Curtin Research Publications
    Abstract

    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

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