Article Dans Une Revue Advances in Data Analysis and Classification Année : 2007

Mixture-model-based signal denoising

Résumé

This paper proposes a new signal denoising methodology for dealing with asymmetrical noises. The adopted strategy is based on a regression model where the noise is supposed to be additive and distributed following a mixture of Gaussian densities. The parameters estimation is performed using a Generalized EM (GEM) algorithm. Experimental studies on simulated and real signals in the context of a diagnosis application in the railway domain reveal that the proposed approach performs better than the least-squares and wavelets methods.

Fichier principal
Vignette du fichier
revision-article-ADAC.pdf (299.08 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

hal-00447038 , version 1 (14-01-2010)

Licence

Identifiants

  • HAL Id : hal-00447038 , version 1

Citer

Allou Samé, Latifa Oukhellou, Etienne Côme, Patrice Aknin. Mixture-model-based signal denoising. Advances in Data Analysis and Classification, 2007, 1 (1), pp.39-51. ⟨hal-00447038⟩
152 Consultations
392 Téléchargements

Partager

  • More