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Two-Kalman Filters Based Instrumental Variable Techniques for Speech Enhancement

Abstract : When a single sequence of noisy observations is available, the AutoRegressive (AR)-model based methods using Kalman-filter make it possible to enhance speech. However, the estimation of the AR parameters is required, but is still a challenging problem as the signal is corrupted by an additive noise. In this paper, we propose to both estimate the signal and the AR parameters by developing a Recursive Instrumental Variable-based approach. Avoiding a non linear approach such as the EKF, this method involves two conditionally linked Kalman filters running in parallel. Once a new observation is available, the first filter uses the latest estimated AR parameters to estimate the signal, while the second filter uses the estimated signal to update the AR parameters. A comparative study between existing speech enhancement methods is completed
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Contributor : Eric Grivel Connect in order to contact the contributor
Submitted on : Wednesday, August 22, 2007 - 2:48:51 PM
Last modification on : Thursday, January 11, 2018 - 6:21:07 AM


  • HAL Id : hal-00167729, version 1


David Labarre, Eric Grivel, Mohamed Najim, Ezio Todini. Two-Kalman Filters Based Instrumental Variable Techniques for Speech Enhancement. MMSP, 2004, Sienne, Italy. pp. ⟨hal-00167729⟩



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