Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2006-03-07
2006-03-07
Hoff, Marc S. (Department: 2857)
Data processing: artificial intelligence
Neural network
Learning task
C702S190000
Reexamination Certificate
active
07010514
ABSTRACT:
The present invention enable to separate source signals from mixture signals into which the source signals are mixed temporally and spatially, where the number of source signals is more than or equal to the number of mixture signals. A signal storing portion12stores the mixture signals input into a signal input portion11,and a formulation portion131in a signal separation portion13extracts the mixture signals stored in the signal storing portion12and formulates them as an operation expression using a basis matrix composed of plural small matrixes that consist of bases with time symmetry. A learning algorithm application portion132applies a learning algorithm based on overcomplete representations, a mixture matrix calculating portion133calculates a mixture matrix, a source signal estimating portion134estimates source signals separated from the mixture signals, and an output portion14outputs the calculated mixture matrix and the estimated source signals.
REFERENCES:
patent: 6424960 (2002-07-01), Lee et al.
patent: 2003/0061035 (2003-03-01), Kadambe
P. Bofill and M. Zibulevsky, “Blind Separation of More Sources Than Mixtures Using Sparsity of Their Short-Time Fourier Transform,” Proc. of ICA Workshop, Jul. 1999, pp 87-92.
L. Q.Zhang et al., “Multichannel Blind Deconvolution of Non-minimum Phase Systems Using Information Backpropagation”, SSN 0042-6989, vol. 37, No. 23, Dec. 1997, In Proceeding of 6thInternational Conference on Neutral Information Processing (ICONIP'99), pp. 210-216, (1999).
B.A. Olshausen et al., “Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by VI?”, USSN-6989, vol. 37, No. 23, pp. 3311-3325, (1997).
B.A. Olshausen et al., “Emergence of simple-cell receptive field properties by learning a sparse code for natural images”, NATURE, vol. 381, pp. 607-609, (Jun. 1996).
B.A. Olshausen et al., “Natural image statistics and efficient coding”, Network, vol. 7, pp. 333-339, (1996).
M.S. Lewicki et al., “Learning Overcomplete Representations”, Neutral Computation, vol. 12, pp. 337-365, (2000).
T.W. Lee, et al., “Blind Source Separation of More Sources Than Mixtures Using Overcomplete Representations”, IEEE Signal Processing Letters, vol. 6, No. 4, Apr. 1999, pp. 87-90.
M.S. Lewicki et al., “Learning nonlinear overcomplete representations for efficient coding”, Advance in Neutral and Information Processing Systems 10, pp. 556-562, (1997),.
Kotani, legal representative Hiroko
Maekawa Satoshi
Okumura Tomoya
Hoff Marc S.
National Institute of Information and Communications Technology
Robbins Janet L
LandOfFree
Blind signal separation system and method, blind signal... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Blind signal separation system and method, blind signal..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Blind signal separation system and method, blind signal... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3555158