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    Persistent audio modelling for background determination

    116820_Persistent%20audio%20modelling%20PID%20116820.pdf (206.9Kb)
    Access Status
    Open access
    Authors
    Moncrieff, Simon
    West, Geoffrey
    Venkatesh, Svetha
    Date
    2005
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Moncrieff, Simon and West, Geoffrey and Venkatesh, Svetha. 2005. Persistent audio modelling for background determination, in SuviSoft Oy Ltd (ed), IEEE International Conference on Multimedia and Expo (ICME 2005), Jul 6 2005, pp. 41-44. Amerstad, Netherlands: IEEE Computer Society.
    Source Title
    Proceedings of IEEE International Conference on Multimedia & Expo (ICME 2005)
    Source Conference
    IEEE International Conference on Multimedia & Expo (ICME 2005)
    DOI
    10.1109/ICME.2005.1521355
    ISBN
    0780393317
    Faculty
    School of Electrical Engineering and Computing
    Department of Computing
    Faculty of Science and Engineering
    Remarks

    Copyright © 2005 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/20.500.11937/41824
    Collection
    • Curtin Research Publications
    Abstract

    This paper is concerned with modelling background audio online to detect foreground sounds in complex audio environments for surveillance and smart home applications. We examine and expand upon previous work in the audio and video domains, and propose a new implementation of an audio background modelling algorithm, addressing the complexities of audio data. A number of audio features characterizing different aspects of the audio content were analysed to determine the factors relevant to the determination of the background audio. We test the algorithms on three audio data sets of varying complexity. The new approach was successful in modelling the background audio for the test data.

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