This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
S. Amaxi, A. Cichocki, and Yang A.A. A new learning algorithm for blind signal separation. In Advances in Neural Information Processing Systems 1995, pages 752–763, Boston, MA, 1996. MIT Press.
S. Amari, A. Cichocki, and Yang A.A. A new learning algorithm for blind signal separation. In Advances in Neural Information Processing Systems 1995, pages 752–763, Boston, MA, 1996. MIT Press.
S. Amari, S.C. Douglas, A. Cichocki, and A.A. Yang. Multichannel blind deconvolution using the natural gradient. In Proc. 1st IEEE Workshop on Signal Processing App. Wireless Comm., Paris, France, 1997. IEEE.
J. Atick and A. Redlich. Towards a theory of early visual processing. Neural Computation, 2:308–320, 1990.
H. Attias and C.E. Schreiner. Blind source separation and deconvolution: Dynamic component analysis. Neural Computations, submitted, 1997.
H. Barlow. Sensory mechanism, the reduction of redundancy, and intelligence. In National Physical Laboratory Symposium, volume 10. Her Majesty's Stationery Office, London, 1959. The Mechanization of Thought Processes.
A. Bell and T. Sejnowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129–1159, 1995.
P. Comon. Independent comonent analysis, a new concept? Signal Processing, 36(3):287–314, 1994.
Gardiner C.W. Handbook of Stochastic Methods. Second Edition. Springer-Verlag, New York, 1990.
G. Deco and Dragan Obradovic. An Information Theoretic Approach to Neural Computing. Perspective in Neural Computing. Springer, 1996.
G. Deco and B. Schuermann. Learning time series evolution by unsupervised extraction of correlations. Physical Review E, 41:1780–1790, 1995.
Yariv Ephraim and Harry Van Trees. A signal subspace approach fot speech enhancement. IEEE Transactions on Speech and Audio Processing, 3(4):251–266, 1995.
H. Hayes, Monson. Statistical Digital Signal Processing and Modeling. Wiley, 1996.
C. Jutten and J. Herault. Blind separation of sources part i: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24(1):1–10, 1991.
M.G. Kendal and A. Stuart. The Advanced Theory of Statistics. Charles Griffin &: Company Limited, London, 1969.
R. Lambert and A. Bell. Blind separation of multiple speakers in a multipath environment. In ICASSP 97, pages 423–426. IEEE, 1997.
T. Lee, A. Bell, and R. Lambert. Blind separation of delayed and convolved sources. In Advances in Neural Information Processing Systems 1996, 1997.
J.S. Lim and A.V. Oppenheim. All-pole modelling of degraded speech. IEEE Transaction ofn Acoustics, Speech, and Signal Processing, 26(3): 197–210, 1979.
R. Linsker. Self-organization in a perceptual network. Computer, pages 105–117, 21.
E. Moulines, J.F. Cardoso, and E. Gassiat. Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. In ICASSP 97, pages 3617–3620. IEEE, 1997.
J. Nadal and N. Parga. Non linear neurons in the low noise limit: a factorial code maximizes information transfere. Network: Computation in Neural Systems, 5(4):565–581, 1994.
B.A. Olhausen and D.J. Field. Emergence of simple-sell receptive field properties by learning sparce code for natural images. Nature, 381:607–609, 1996.
A.V. Oppenheim and R.W. Schafer. Discret-Time Signal Processing. Prentice Hall, 1989.
L. Parra. Symplectic nolinear component analysis. In Advances in Neural Information Processing Systems 1995, pages 437–443, Boston, MA, 1996. MIT Press.
L. Parra, G. Deco, and S. Miesbach. Redundancy reduction with information preserving nonlinear maps. Network: Computation in Neural Systems, 6:61–72, 1995.
B. Pearlmutter and L. Parra. A context-sencitive generalization of independent component analysis. In International Conf. on Neural Information Processing, Hong Kong, 1996.
B. Pearlmutter and L. Parra. Maximum likelihood source separation: a context-sensitive generalization of ica. In NIPS 96, Hong Kong, 1997.
D.T. Pham, Garrat P., and Jutten C. Separation of a mixture of independent sources through a maximum likelihood approach. In Proc. EUSIPCO, pages 771–774, 1992.
ML subspace signal enhancement. Fast adaptive eigenvalue decomposition: A maxmum likelihood approach. In ICASSP 97. IEEE, 1997.
Charles W. Therrien. Discrete Random Signals and Statistical Signal Processing. Prentice Hall, 1992.
E. Weinstein, M. Feder, and A.V. Oppenheim. Multi-channel signal separation by decorrelation. IEEE Transaction on Spreech and Audio Processing, 1(4):405–413, 1993.
D. Yellin and E. Weinstein. Multichannel signal separation: Methods and analysis. IEEE Transaction on Signal Processing, 44(1):106–118, 1996.
J. Zhu, X.R. Cao, and R.W. Liu. Blind source separation based on output independence — theory and implementation. In NOLTA 95, volume 1, pages 97–102, Tokyo, Japan, 1995. NTA Research Society of IEICE.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Parra, L.C. (1998). Temporal models in blind source separation. In: Giles, C.L., Gori, M. (eds) Adaptive Processing of Sequences and Data Structures. NN 1997. Lecture Notes in Computer Science, vol 1387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054001
Download citation
DOI: https://doi.org/10.1007/BFb0054001
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64341-8
Online ISBN: 978-3-540-69752-7
eBook Packages: Springer Book Archive