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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Electrical Engineering and Information Science Journal of Electrical Engineering and Information Science Vol.4 No.4
- 발행연도
- 1999.8
- 수록면
- 499 - 506 (8page)
이용수
초록· 키워드
오류제보하기
Source separation is a statistical method, the goal of which is to recover mutually independent sources from their linear instantaneous mixtures without resorting to any prior knowledge. Most existing methods have been focused on stationary sources, so higher-order statistics was necessary for separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [24] that source separation could be achieved by only decorrelation (second-order statistics). In this paper, we adopt the natural gradient method [1] and derive efficient source separation algorithms by minimizing the cost function proposed by Matsuoka et al. Useful behavior of proposed algorithms are demonstrated through computer simulations.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Problem Formulation and Model Assumptions
Ⅲ. Cost Function
Ⅳ. Learning Algorithms
Ⅴ. Computer Simulation Results
Ⅵ. Conclusions
Acknowledgements
References
저자소개
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UCI(KEPA) : I410-ECN-0101-2009-569-017770028