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Circuits, Systems, and Signal Processing

, Volume 32, Issue 5, pp 2317–2334 | Cite as

Edge Effect Elimination in Single-Mixture Blind Source Separation

  • Yina GuoEmail author
  • Shuhua Huang
  • Yongtang Li
  • Ganesh R. Naik
Article

Abstract

Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends’ extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE).

Keywords

Blind source separation (BSS) Independent component analysis (ICA) Principal component analysis (PCA) Ensemble empirical mode decomposition (EEMD) Edge effect 

Notes

Acknowledgements

Funding for this work was supported by 2010 research project of Shanxi Scholarship Council of China [Nos. 92, 20101069], 2011 research project of Department of Human Resources and Social Security of Shanxi Province [No. 20121030], 2012 The ShanXi Science and Technology Department [No. 2012081036], 2012 graduate students’ innovative project of Department of Science and Technology of Taiyuan City [No. 120164034] and 2010 Youth Foundation of Taiyuan University of Science and Technology of China [No. 20103004].

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yina Guo
    • 1
    Email author
  • Shuhua Huang
    • 1
  • Yongtang Li
    • 1
  • Ganesh R. Naik
    • 2
  1. 1.Taiyuan University of Science and TechnologyTaiyuanChina
  2. 2.RMIT UniversityMelbourneAustralia

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