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    Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework

    Lin S 2019.pdf (4.999Mb)
    Access Status
    Open access
    Authors
    Lin, Shoufeng
    Date
    2019
    Supervisor
    Ling Li
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/76069
    Collection
    • Curtin Theses
    Abstract

    The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods.

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