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  • Robustness metric-based tuning of the augmented Kalman filter for the enhancement of speech corrupted with coloured noise

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    Accepted Manuscript (AM)
    Author(s)
    George, Aidan EW
    So, Stephen
    Ghosh, Ratna
    Paliwal, Kuldip K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    So, Stephen
    Year published
    2018
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    Abstract
    In this paper, we describe a tuning method based on a robustness metric and extended to work with the augmented Kalman filter for enhancing coloured-noise-corrupted speech. The method proposed within utilises the robustness metric to provide dynamic and adaptive tuning of the Kalman filter gain in order to reduce the residual noise that results from poor speech model estimates. An analysis of the Kalman filter recursion equations is presented that augments the robustness metric equations to include coloured noise model parameters. Objective and blind AB subjective listening tests were performed on the NOIZEUS speech corpus ...
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    In this paper, we describe a tuning method based on a robustness metric and extended to work with the augmented Kalman filter for enhancing coloured-noise-corrupted speech. The method proposed within utilises the robustness metric to provide dynamic and adaptive tuning of the Kalman filter gain in order to reduce the residual noise that results from poor speech model estimates. An analysis of the Kalman filter recursion equations is presented that augments the robustness metric equations to include coloured noise model parameters. Objective and blind AB subjective listening tests were performed on the NOIZEUS speech corpus for both white and coloured noises with the results being compared with the MMSE method. In the blind AB subjective testing, the 15 English-speaking listeners showed preference for the proposed method over both the MMSE and oracle Kalman filter methods (where clean speech parameters were used). These results imply that the proposed tuned Kalman filter produces more perceptibly-acceptable enhanced speech than the oracle Kalman filter, which is considered the ideal for this enhancement technique.
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    Journal Title
    Speech Communication
    Volume
    105
    DOI
    https://doi.org/10.1016/j.specom.2018.10.002
    Copyright Statement
    © 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial intelligence
    Signal processing
    Cognitive and computational psychology
    Linguistics
    Communications engineering
    Publication URI
    http://hdl.handle.net/10072/382193
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    • Journal articles

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