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A dual Kalman filter-based smoother for speech enhancement

Abstract : Kalman algorithms have been widely applied, for instance in single-channel speech enhancement. However, when carrying out Kalman smoothing, computational cost and data storage requirements are two specific problems. A dual-filter-based smoother is proposed and used in the framework of speech enhancement. Our approach comprises a forward-in-time Kalman filter and a backward-in-time Kalman filter. Both filters are based on their respective forward-in-time linear prediction (LP) model and backward-in-time LP model. This method does not require as large a storage space as a standard Kalman smoother does. The algorithm is evaluated by considering a speech signal embedded in a white Gaussian noise. Simulation results show that the proposed algorithm provides a higher improvement of signal-to-noise ratio (SNR) than Kalman filtering.
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Contributor : Eric Grivel <>
Submitted on : Wednesday, August 22, 2007 - 3:58:56 PM
Last modification on : Thursday, January 11, 2018 - 6:21:07 AM


  • HAL Id : hal-00167755, version 1


Hong Cai, Eric Grivel, Mohamed Najim. A dual Kalman filter-based smoother for speech enhancement. ICASSP, 2003, Hong Kong, China. pp. 912-915. ⟨hal-00167755⟩



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