Mixture-model-based signal denoising

Allou Samé (1) , Latifa Oukhellou (1) , Etienne Côme (1) , Patrice Aknin (1)

1 INRETS/LTN - Laboratoire des Technologies Nouvelles

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.

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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⟩