Mixture-model-based signal denoising

被引：0
|
作者：
Allou Samé
Latifa Oukhellou
Etienne Côme
Patrice Aknin
机构：
[1] Institut National de Recherche sur les Transports et leur Sécurité (INRETS),CERTES
[2] Université Paris XII,undefined
来源：
Advances in Data Analysis and Classification | 2007年 / 1卷
关键词：
Denoising; Asymmetrical noise; Regression; Gaussian mixture model; EM Algorithm; GEM algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：39 / 51
页数：12
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