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A novel grading noise-pretreatment algorithm based on time-frequency blind source separation

  • Wang Er-Fu*
  • , Zhang Nai-Tong
  • , Meng Wei-Xiao
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The blind source separation (BSS) problem under noise is known as a hard problem. The performance of separation algorithm degrades with the decrease of SNR significantly. The key solution is the noise pretreatment. Wavelet transform (WT) and empirical mode decomposition (EMD), two typical analysis methods especially for the processing practical nonstationarity signals in time-frequency domain, are chosen as the pretreatment methods in this paper. Based on the analysis of the denoising performances by the two methods, a grading noise-pretreatment project is proposed which automatically selects a method according to different SNR. Simulation results shows that such flexible scheme could enhance the BSS performance by effectively denoising, and also makes the existing blind source separation apply to larger range of SNR and enhances the robustness of algorithm.

Original languageEnglish
Title of host publicationProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008
Pages1225-1228
Number of pages4
DOIs
StatePublished - 2008
Event2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008 - Harbin, China
Duration: 15 Aug 200817 Aug 2008

Publication series

NameProceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008

Conference

Conference2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008
Country/TerritoryChina
CityHarbin
Period15/08/0817/08/08

Keywords

  • Blind Source Separation
  • Empirical Mode Decomposition
  • Grading Noise-pretreatment
  • Time-frequency Analysis
  • Wavelet Transform

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