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    Tracking and separation of multiple moving speech sources via cardinality balanced multi-Target multi Bernoulli (CBMeMBer) filter and time frequency masking

    Access Status
    Fulltext not available
    Authors
    Chong, Nicholas
    Nordholm, Sven
    Vo, Ba Tuong
    Murray, Iain
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Chong, N. and Nordholm, S. and Vo, B.T. and Murray, I. 2017. Tracking and separation of multiple moving speech sources via cardinality balanced multi-Target multi Bernoulli (CBMeMBer) filter and time frequency masking, 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), 27-29 Oct. 2016, pp. 88-93: IEEE.
    Source Title
    2016 International Conference on Control, Automation and Information Sciences (ICCAIS)
    DOI
    10.1109/ICCAIS.2016.7822441
    ISBN
    9781509006502
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/50768
    Collection
    • Curtin Research Publications
    Abstract

    In a 'conference room scenario', the number of speech sources are not known a priori and the number of speech sources which are active remains unknown as these speech sources appear and disappear throughout the measurement period. Furthermore, the speech sources are moving so their mixing parameters change with time. As a result of this, traditional source separation techniques are limited by their capability to properly attribute the correct mixing parameters to the respective sources. The 'conference room scenario' problem is very challenging as it involves the localization, tracking and separation of a time varying number of moving speech sources. An online solution which systematically solves 'conference room scenario' problem by solving the source localization, tracking and separation in stages is proposed in this paper.

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