지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
오류제보하기
Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to sol ve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.
목차
Abstract
1. Introduction
2. Mutual Information
3. Learning of Neural Networks
4. Computer Simulation
5. Conclusions
Appendix
Acknowledgement
References
저자소개
참고문헌 (23)
참고문헌 신청
1. [학술지(정기간행물)] - G. Huang / 2004 / A radar anti‐jamming technology based on blind source separation / Proc. of Int. Conf. on Signal Processing 3 : 2021 ~ 2024
2. [학술지(정기간행물)] - E. Visser / 2003 / Speech enhancement using blind source separation and two‐channel energy based speaker detection / Proc. of IEEE Int. Conf. on Acoustics Speech & Signal Processing 1 : 884 ~ 887
3. [학술지(정기간행물)] - Q. Pan / 2004 / A post‐filter perceptual convolutive blind source separation approach for speech signal / Proc. of Int. Conf. on Signal Processing 1 : 327 ~ 330
4. [학술대회논문] - D. Kim / 2003 / Acoustic echo cancellation using blind source separation / IEEE Workshop on Signal Processing Systems : 241 ~ 244
5. [학술지(정기간행물)] - C. W. Hesse / 2006 / On semi‐blind source separation using spatial constraints with applications in EEG analysis / IEEE Trans. on Biomedical Engineering 53 (12) : 2525 ~ 2534
2. [학술지(정기간행물)] - E. Visser / 2003 / Speech enhancement using blind source separation and two‐channel energy based speaker detection / Proc. of IEEE Int. Conf. on Acoustics Speech & Signal Processing 1 : 884 ~ 887
3. [학술지(정기간행물)] - Q. Pan / 2004 / A post‐filter perceptual convolutive blind source separation approach for speech signal / Proc. of Int. Conf. on Signal Processing 1 : 327 ~ 330
4. [학술대회논문] - D. Kim / 2003 / Acoustic echo cancellation using blind source separation / IEEE Workshop on Signal Processing Systems : 241 ~ 244
5. [학술지(정기간행물)] - C. W. Hesse / 2006 / On semi‐blind source separation using spatial constraints with applications in EEG analysis / IEEE Trans. on Biomedical Engineering 53 (12) : 2525 ~ 2534
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UCI(KEPA) : I410-ECN-0101-2009-560-019072939