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  • An Iterative Kalman Filter with Reduced-Biased Kalman Gain for Single Channel Speech Enhancement in Non-stationary Noise Condition

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    Author(s)
    Roy, Sujan Kumar
    Paliwal, Kuldip K
    Griffith University Author(s)
    Roy, Sujan Kumar K.
    Paliwal, Kuldip K.
    Year published
    2019
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    Abstract
    This paper presents an iterative Kalman filter (IT-KF) with a reduced-biased Kalman gain for single channel speech enhancement in Non-stationary Noise Conditions (NNCs). The proposed IT-KF aims to offset the bias in Kalman gain through efficient parameter estimation leading to improve the speech enhancement performance. To do this, we introduce a Decision Directed (DD) and a posteriori SNR based noise variance estimation method controlled through Speech Activity Detector (SAD). The proposed SAD incorporates a majority voting of three distinct SAD fusions. The LPC parameters are computed from the pre-smoothing of noisy speech. ...
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    This paper presents an iterative Kalman filter (IT-KF) with a reduced-biased Kalman gain for single channel speech enhancement in Non-stationary Noise Conditions (NNCs). The proposed IT-KF aims to offset the bias in Kalman gain through efficient parameter estimation leading to improve the speech enhancement performance. To do this, we introduce a Decision Directed (DD) and a posteriori SNR based noise variance estimation method controlled through Speech Activity Detector (SAD). The proposed SAD incorporates a majority voting of three distinct SAD fusions. The LPC parameters are computed from the pre-smoothing of noisy speech. With these initial estimated parameters, an IT-KF processes the noisy speech at first iteration. The parameters are re-estimated from the processed speech, re-adjust the Kalman gain, and the process is repeated at second iteration. It is shown that the adjusted Kalman gain enables the IT-KF to minimize the remaining artifacts of the processed speech, yielding the enhanced speech. Extensive simulation results reveal that the proposed method outperforms other benchmark methods in NNCs for a wide range of SNRs.
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    Journal Title
    International Journal of Signal Processing Systems
    Volume
    7
    Issue
    1
    DOI
    https://doi.org/10.18178/ijsps.7.1.7-13
    Copyright Statement
    © The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/396485
    Collection
    • Journal articles

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