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Mixture-model-based signal denoising

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Abstract

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.

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Authors and Affiliations

  1. Institut National de Recherche sur les Transports et leur Sécurité (INRETS), 2 avenue du général Malleret-Joinville, 94114, Arcueil, France

    Allou Samé, Latifa Oukhellou, Etienne Côme & Patrice Aknin

  2. CERTES, Université Paris XII, 61 avenue du général De Gaulle, 94100, Créteil, France

    Latifa Oukhellou

Authors
  1. Allou Samé
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  2. Latifa Oukhellou
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  3. Etienne Côme
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  4. Patrice Aknin
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Correspondence to Patrice Aknin.

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Samé, A., Oukhellou, L., Côme, E. et al. Mixture-model-based signal denoising. ADAC 1, 39–51 (2007). https://doi.org/10.1007/s11634-006-0002-8

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  • DOI: https://doi.org/10.1007/s11634-006-0002-8

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