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

Sujan Kumar Roy and Kuldip K. Paliwal
Signal Processing Laboratory, Griffith School of Engineering, Griffith University, Brisbane, QLD, Australia, 4111
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. 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. 

Index Terms—speech enhancement, kalman filter, non-stationary noise, speech activity detector, pre-smoothing

Cite: Sujan Kumar Roy and Kuldip K. Paliwal, "An Iterative Kalman Filter with Reduced-Biased Kalman Gain for Single Channel Speech Enhancement in Non-stationary Noise Condition," International Journal of Signal Processing Systems, Vol. 7, No. 1, pp. 7-13, March 2019. doi: 10.18178/ijsps.7.1.7-13

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