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Two-Kalman filters based instrumental variable techniques for speech enhancement

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4 Author(s)
Labarre, D. ; Universite de Bordeaux 1, Talence, France ; Grivel, E. ; Najim, Mohamed ; Todini, E.

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.

Published in:

Multimedia Signal Processing, 2004 IEEE 6th Workshop on

Date of Conference:

29 Sept.-1 Oct. 2004