메뉴 건너뛰기




Volumn 1, Issue , 2000, Pages 233-236

Adaptive proccessing of blind source separation through 'ica with os'

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COST FUNCTIONS; COSTS; MIXTURES; SIGNAL PROCESSING; ACOUSTIC SIGNAL PROCESSING; MATRIX ALGEBRA; OPTIMIZATION; PROBLEM SOLVING; STATISTICS; VECTORS;

EID: 0033677129     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2000.861927     Document Type: Conference Paper
Times cited : (3)

References (12)
  • 2
    • 0028416938 scopus 로고
    • Independent component analysis, A new concept
    • Pierre Common
    • Pierre Common . Independent component analysis, A new concept. Signal Processing, 36(1994) 287-314.
    • (1994) Signal Processing , vol.36 , pp. 287-314
  • 3
    • 0026191274 scopus 로고    scopus 로고
    • Blind separation of sources, part I: An adapative procedure based on neuromimetic architecture
    • C.Jutten and J. Herault. Blind separation of sources, part I: An adapative procedure based on neuromimetic architecture . Signal processing, 24, vol 1.page 1-10.
    • Signal Processing , vol.1 , Issue.24 , pp. 1-10
    • Jutten, C.1    Herault, J.2
  • 4
    • 0002049291 scopus 로고
    • Separation of a mixture of independent sources through a maximum likelihood approach
    • D. T. Pham, P. Gharat, C. Jutten. Separation of a mixture of independent sources through a maximum likelihood approach. Proc. EUSIPCO 1992, 771-774.
    • (1992) Proc. EUSIPCO , pp. 771-774
    • Pham, D.T.1    Gharat, P.2    Jutten, C.3
  • 5
    • 0029411030 scopus 로고    scopus 로고
    • An information maximization approach to Blind Separation and Blind Deconvolution
    • A.J. Bell and T.J Sejnowski . An information maximization approach to Blind Separation and Blind Deconvolution. Neural Computation, 7, 6, 1129-1159.
    • Neural Computation , vol.7 , Issue.6 , pp. 1129-1159
    • Bell, A.J.1    Sejnowski, T.J.2
  • 6
    • 0343416807 scopus 로고    scopus 로고
    • The non linear pea learning rule in independent component analysis
    • E. Oja . The non linear pea learning rule in independent component analysis. Neurocomputing, 17. 1997 page 25-45.
    • (1997) Neurocomputing , vol.17 , pp. 25-45
    • Oja, E.1
  • 9
    • 33749915527 scopus 로고    scopus 로고
    • Adaptive separation of independent sources: A deflation approach
    • N. Delfosse, P. Loubaton. Adaptive separation of independent sources: A deflation approach. Proc. ICASSP, 94. page IV41-IV44.
    • Proc. ICASSP , vol.94 , pp. IV41-IV44
    • Delfosse, N.1    Loubaton, P.2
  • 10
    • 84892171190 scopus 로고    scopus 로고
    • Extraction of independent components from hybrid mixture: Knicnet learning algorithm and applications
    • 1209
    • S.Y. Kung, C.Mejuto. Extraction of Independent Components from Hybrid mixture: knicnet learning algorithm and applications.ICASSP'98 . vol 11, 1209, 1211.
    • ICASSP'98 , vol.11 , pp. 1211
    • Kung, S.Y.1    Mejuto, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.