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  • Sensitivity Metric-Based Tuning of the Augmented Kalman Filter for Speech Enhancement

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    Roy457191-Accepted.pdf (1.309Mb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Roy, Sujan Kumar
    Paliwal, Kuldip K
    Griffith University Author(s)
    Roy, Sujan Kumar K.
    Paliwal, Kuldip K.
    Year published
    2020
    Metadata
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    Abstract
    The state-of-the-art robustness metric-based tuning of the augmented Kalman filter (AKF) gives an under-estimated Kalman gain, resulting distortion in the enhanced speech during colored noise suppression. This paper introduces a sensitivity metric-based tuning of the AKF for enhancing speech corrupted with different noises. Specifically, we observe that the sensitivity metric-based tuning of the AKF overcomes the under-estimation issues of Kalman gain in the existing method. It is shown that the reduced-biased Kalman gain enables the AKF to restrict the residual noise passed to the enhanced speech. It also minimizes the ...
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    The state-of-the-art robustness metric-based tuning of the augmented Kalman filter (AKF) gives an under-estimated Kalman gain, resulting distortion in the enhanced speech during colored noise suppression. This paper introduces a sensitivity metric-based tuning of the AKF for enhancing speech corrupted with different noises. Specifically, we observe that the sensitivity metric-based tuning of the AKF overcomes the under-estimation issues of Kalman gain in the existing method. It is shown that the reduced-biased Kalman gain enables the AKF to restrict the residual noise passed to the enhanced speech. It also minimizes the distortion in the enhanced speech. Objective and subjective testing on NOIZEUS corpus reveal that the enhanced speech produced by the proposed method exhibits higher quality as well as intelligibility than the benchmark methods in colored and non-stationary noise conditions for a wide range of SNR levels.
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    Conference Title
    2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS)
    DOI
    https://doi.org/10.1109/icspcs50536.2020.9310005
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Signal Processing
    Publication URI
    http://hdl.handle.net/10072/400836
    Collection
    • Conference outputs

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