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Communication Dans Un Congrès Année : 2004

Two-Kalman Filters Based Instrumental Variable Techniques for Speech Enhancement

David Labarre
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Ezio Todini
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Résumé

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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Dates et versions

hal-00167729 , version 1 (22-08-2007)

Identifiants

  • HAL Id : hal-00167729 , version 1

Citer

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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