Paper Title:
AR Parameters-Based Nonlinear Blind Source Extraction
  Abstract

In nonlinear blind source separation (BSS) independence is not sufficient to recover the original source signal and additional criteria are needed to sufficiently constrain the optimization problem. Here we introduce autoregressive (AR) parameters as criteria and combined with expansion space develop a new method, which lead to a unique solution of the nonlinear BSS problem. The proposed method is based on two key assumptions. One lies in that a source signal’s AR parameters can be roughly estimated before operation, and the other is that expansion space, such as kernel feature space, should be chosen rich enough to approximate the nonlinearity. This method can extract the desired source signal as a unique solution with the help of this signal’s AR parameter, or it extracts one signal at one time. Thus it is also referred to as nonlinear blind source extraction (BSE). Its performance is demonstrated on nonlinearly mixed speech data.

  Info
Periodical
Edited by
Qi Luo
Pages
1129-1135
DOI
10.4028/www.scientific.net/AMM.20-23.1129
Citation
Y. Cai, G. Wang, "AR Parameters-Based Nonlinear Blind Source Extraction ", Applied Mechanics and Materials, Vols. 20-23, pp. 1129-1135, 2010
Online since
January 2010
Authors
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Meng Di Ye, Zhi Nong Li, Yao Xian Xiao, Xu Ping He, Jing Wen Yan
Chapter 6: Applied Mechanics and Fault Diagnosis Analysis
Abstract:A nonlinear blind separation method of mechanical fault sources is proposed. In the proposed method, the signal is transformed from the...
394
Authors: Hong Kun Li, Hong Yi Liu, Chang Bo He
Chapter 4: Algorithms and Methods of Data and Signal Processing in Technique of Measurements and Fault Detection
Abstract:Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration...
1350