• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • A Kalman filtering algorithm with joint metrics-based tuning for single-channel speech enhancement

    Thumbnail
    View/Open
    GeorgePUB2611.pdf (4.972Mb)
    Author(s)
    George, Aidan
    So, Stephen
    Ghosh, Ranadhir
    Paliwal, Kuldip
    Griffith University Author(s)
    Ghosh, Ranadhir
    Paliwal, Kuldip K.
    So, Stephen
    George, Aidan E.
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    In this paper, we present an iterative Kalman filtering algorithm that exhibits better speech enhancement by jointly utilising robustness and sensitivity metrics. Typically, poor model parameter estimates lead to a biased Kalman filter gain, which results in innovation noise ‘leaking’ into the output. In the proposed algorithm, the Kalman filter gain is dynamically tuned based on a varying operating point of balanced robustness and sensitivity. Speech enhancement experiments showed the proposed Kalman filtering algorithm to produce higher quality speech than conventional methods using objective and subjective measures.In this paper, we present an iterative Kalman filtering algorithm that exhibits better speech enhancement by jointly utilising robustness and sensitivity metrics. Typically, poor model parameter estimates lead to a biased Kalman filter gain, which results in innovation noise ‘leaking’ into the output. In the proposed algorithm, the Kalman filter gain is dynamically tuned based on a varying operating point of balanced robustness and sensitivity. Speech enhancement experiments showed the proposed Kalman filtering algorithm to produce higher quality speech than conventional methods using objective and subjective measures.
    View less >
    Conference Title
    Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology
    Publisher URI
    http://www.assta.org/?q=sst-conferences
    Copyright Statement
    © 2016 ASSTA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Signal Processing
    Publication URI
    http://hdl.handle.net/10072/100784
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander