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N. Bouguerriou, M. Haritopoulos, C. Capdessus, and L. Allam, “Novel cycostationarity based blind source separation algorithm using second order statistical properties: Theory and application to the bearing defect diagnosis,” Mechanical Systems and Signal Processing, No. 19, pp. 1260–1281, 2005.

has been cited by the following article:

• TITLE: Research on Blind Source Separation for Machine Vibrations

  AUTHORS: Weiguo HUANG, Shuyou WU, Fangrang KONG, Qiang WU

  KEYWORDS: Blind Source Separation, Independent Component Analysis, Convolutive Mixtures, Machine Vibration, Residual Cross-Talking Error

  JOURNAL NAME: Wireless Sensor Network, Vol.1 No.5, December 16, 2009

  ABSTRACT: Blind source separation is a signal processing method based on independent component analysis, its aim is to separate the source signals from a set of observations (output of sensors) by assuming the source signals independently. This paper reviews the general concept of BSS firstly; especially the theory for convolutive mixtures, the model of convolutive mixture and two deconvolution structures, then adopts a BSS algorithm for convolutive mixtures based on residual cross-talking error threshold control criteria, the simulation testing points out good performance for simulated mixtures.