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    Bayesian noise estimation in the modulation domain

    Access Status
    Fulltext not available
    Authors
    Singh, M.
    Low, S.
    Nordholm, Sven
    Zang, Z.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Singh, M. and Low, S. and Nordholm, S. and Zang, Z. 2018. Bayesian noise estimation in the modulation domain. Speech Communication. 96: pp. 81-92.
    Source Title
    Speech Communication
    DOI
    10.1016/j.specom.2017.11.008
    ISSN
    0167-6393
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/60895
    Collection
    • Curtin Research Publications
    Abstract

    Modulation domain has been reported to be a better alternative to time-frequency domain for speech enhancement, as speech intelligibility is closely linked with the modulation spectrum. Motivated by that, this paper investigates the use of modulation domain to model the noise density function. Results show that the modulation domain based Gamma density function better represents the noise density for all time-varying noise signals compared to the non-modulation domain. The modulation based Gamma density is then used to derive noise estimator via a Bayesian motivated MMSE approach. As the Gamma density closely matches the true noise spectrum in the modulation domain, the proposed noise estimator does not require bias compensation even for poor signal-to-noise ratio (SNR) conditions, i.e., = 5 dB. The proposed method yields better noise suppression compared to the state of the art methods and provides higher improvements.

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