Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Comparison of Independent Component Analysis and Blind Source Separation Algorithms for Noisy Data

잡음환경에서 독립성분 분석과 암묵신호분리 알고리즘의 성능비교

  • 오상훈 (목원대학교 전자정보통신공학부) ;
  • ;
  • 최승진 (포항공대 컴퓨터공학과) ;
  • 이수영 (한국과학기술원 뇌과학연구센터)
  • Published : 2002.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.

여러 가지의 독립성분분석 및 암묵신호분리 알고리즘들이 개발되었지만, 아직 이러한 알고리즘들의 성능비교가 철저히 이루어지지는 못 하였다. 이 논문은 이 알고리즘들 중에서 뛰어난 알고리즘들을 센서 잡음에 대한 강인성, 계산 복잡도, 혼합 행렬의 조건, 센서 수, 학습패턴 수 등 여러 측면에서 비교한다. 또한, 알고리즘들의 성능 비교에 유용한 문제들도 제시한다. 이 비교결과는 이 알고리즘들의 EEG/MEG 분석, 음성신호분리 등과 같은 실질적 응용에 큰 도움이 될 것이다.

Keywords

References

  1. S. Choi and A. Cichocki, 'Blind separation of nonstationary sources in noisy mixtures,' IEE Electronics Letters, Vol. 36, No. 9, pp. 848-849, April 27, 2000 https://doi.org/10.1049/el:20000623
  2. A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, E. Moulines, 'A blind source separation technique using second-order statictics,' IEEE Trans. Signal Processing, Vol. 45, No. 2, pp. 434-444, February 1997 https://doi.org/10.1109/78.554307
  3. T.-W. Lee, M. Girolami, A. J. Bell, T. J. Sejnowski, 'A unifying information-theoretic framework for Independent Component Analysis.' Computers & Mathematics with Applications, Vol. 31, No. 11, pp. 1-21, March 2000 https://doi.org/10.1016/S0898-1221(00)00101-2
  4. A. Hyvarinen and E. Oja, 'A fast fixed-point algorithm for Independent Component Analysis,' Neural Computation, Vol. 9, pp. 1483-1492, 1997 https://doi.org/10.1162/neco.1997.9.7.1483
  5. A. Hyvarinen, 'Fast and robust fixed-point algorithms for Independent Component Analysis,' IEEE Trans. Neural Networks, Vol. 10, No. 3, pp. 626-634, May 1999 https://doi.org/10.1109/72.761722
  6. S. Amari, 'Neural Computation,Natural gradient works efficiently in learningf,' Neural Computation, pp. 251-276, Feb. 1998 https://doi.org/10.1162/089976698300017746
  7. A. Cichock and R. Unbehauen, 'Robust neural networks with on-line learning for blind identification and blind separation of sources,' IEEE Trans. Circuits and Systems-I: Fundamental Theory and Applications, Vol. 43, pp. 894-906, Feb. 1996 https://doi.org/10.1109/81.542280
  8. S. Choi, A. Cichocki, and S. Amari, 'Flexible independent component analysis,' J. VLSI Signal Processing-Systems for Signal, Image, and Video technology, Vol. 26, pp. 25-38, August 2000 https://doi.org/10.1023/A:1008135131269
  9. S. Cruces, A. Cichocki, and L. Castedo, 'Blind source extraction in Gaussian noise,' Proc. ICA'2000, Helsinki, Finland, pp. 63-68, June 2000
  10. A. J. Bell jand T. J. Sejnowski, 'An information-maximisation approach to blind separation and blind deconvolution,' Neural Computation, Vol. 7, No. 6, pp. 1004-1034, 1995
  11. T.-W. Lee, M. Girolami, and T. J. Sejnowski, 'Independent Component Analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources,' Neural Computation, Vol. 11, pp. 417-441, 1999 https://doi.org/10.1162/089976699300016719
  12. A. Hyvarinen, 'Noisy independent component analysis by Gaussian moments,' Proc. ICA'99, Aussois, France, pp. 473-478, 1999
  13. S. C. Douglas and X. Sun, 'Blind signal separation of arbitrary mixtures: adaptive algorithms and stability analysis,' Proc. ICA2000, Helsinki, Finland, pp. 163-168, June 2000