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
相关论文
共 50 条
  • [41] Wavelet based image denoising based on a mixture of Laplace distributions
    Rabbani, H.
    Vafadoost, M.
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B-ENGINEERING, 2006, 30 (B6): : 711 - 733
  • [42] Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder
    Lu, Haifei
    Zhou, Kedong
    He, Lei
    ELECTRONICS, 2024, 13 (12)
  • [43] Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based On Wavelet Denoising Technique
    Zhu, Zhang Chao
    Abdullah, Ahmed Kareem
    JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING, 2015, 23 : 9 - 17
  • [44] Discussion of approach for extracting pure EOG Reference signal from EEG mixture based on wavelet denoising technique
    Zhu, Zhang Chao
    Abdullah, Ahmed Kareem
    Journal of Biomimetics, Biomaterials and Biomedical Engineering, 2015, 23 : 9 - 17
  • [45] UNSUPERVISED ANALYSIS OF A CHROMATOGRAPHIC SIGNAL BASED ON AN INFINITE GAUSSIAN MIXTURE MODEL
    Harant, O.
    Bertholon, F.
    Foan, L.
    Vignoud, S.
    Grangeat, P.
    2017 ICOCS/IEEE INTERNATIONAL SYMPOSIUM ON OLFACTION AND ELECTRONIC NOSE (ISOEN 2017), 2017,
  • [46] Detection of Unmanned Aerial Vehicle Signal Based on Gaussian Mixture Model
    Zhao, Caidan
    Shi, Mingxian
    Cai, Zhibiao
    Chen, Caiyun
    2017 12TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND EDUCATION (ICCSE 2017), 2017, : 289 - 293
  • [47] Residual Learning Based RF Signal Denoising
    Wang, Yongshi
    Tu, Lan
    Guo, Jie
    Wang, Zhigang
    PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ), 2018, : 15 - 18
  • [48] Signal Denoising Based on Duffing Oscillators System
    Luo, Wenmao
    Cui, Yingliu
    IEEE ACCESS, 2020, 8 : 86554 - 86563
  • [49] Denoising of LAPS signal based on wavelet transform
    Li, X.-L. (liushibin@nwpu.edu.cn), 1600, Board of Optronics Lasers (25):
  • [50] Improved VC-based signal denoising
    Shao, J
    Cherkassky, V
    IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 2439 - 2444