Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
2007-10-09
2007-10-09
McElheny, Jr., Donald E. (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C381S313000
Reexamination Certificate
active
10809285
ABSTRACT:
Systems and methods for performing source separation are provided. Source separation is performed using a composite signal and a signal dictionary. The composite signal is a mixture of sources received by a sensor. The signal dictionary is a database of filtered basis functions that are formed by the application of directional filters. The directional filters approximate how a particular source will be received by the sensor when the source originates from a particular location. Each source can be characterized by a coefficient and a filtered basis function. The coefficients are unknown when the sources are received by the sensor, but can be estimated using the composite signal and the signal dictionary. Various ones of the sources may be selectively reconstructed or separated using the estimated value of the coefficients.
REFERENCES:
patent: 5325436 (1994-06-01), Soli et al.
patent: 5793875 (1998-08-01), Lehr et al.
patent: 6002776 (1999-12-01), Bhadkamkar et al.
patent: 6285766 (2001-09-01), Kumamoto
patent: 6317703 (2001-11-01), Linsker
patent: 6526148 (2003-02-01), Jourjine et al.
patent: 6751325 (2004-06-01), Fischer
patent: 6950528 (2005-09-01), Fischer
patent: 6963649 (2005-11-01), Vaudrey et al.
patent: 6987856 (2006-01-01), Feng et al.
patent: 7142677 (2006-11-01), Gonopolskiy et al.
patent: 7149320 (2006-12-01), Haykin et al.
patent: 2005/0060142 (2005-03-01), Visser et al.
Jang et al., A Maximum Likelihood Approach to Single-Channel Source Separation, Dec. 2003, Journal of Machine Learning Research, vol. 4, pp. 1365-1392.
Jang et al., A Subspace Approach to Single Channel Separation Using Maximum Likelihood Weighting Filters, 2003 IEEE, pp. 45-48.
Delfosse et al., Adaptive Blind Separation of Convolutive Mixtures, 1996 IEEE, pp. 341-345.
Aichmer et al., Time Domain Blind Source Separation of Non-Stationary Convolved Signals by Utilizing Geometric Beamforming, 2002 IEEE, pp. 445-454.
Bell, Anthony, et al., “The ‘Independent Components’ of Natural Scenes are Edge Filters”, Vision Research, vol. 37(23), pp. 3327-3338, 1997.
Bofill, Paul, et al., “Underdetermined Blind Source Separation Using Sparse Representations”, Signal Processing, vol. 81(11), pp. 2353-2362, 2001.
Cauwenbergs, G., “Monaural Separation of Independent Acoustical Components”, In Proceeding IEEE International Symposium on Circuits and Systems (ISCSS'99), Orlando, Florida, vol. 5 of 6, pp. 62-65, 1999.
Chen, Scott Shaobing, et al., “Atomic Decomposition by Basis Pursuit”, SIAM Journal on Scientific Computing, vol. 20(1), pp. 33-61, 1999.
Donoho, D.L., et al., “Optimally Sparse Representation in General (nonorthogonal) dictionaries via l1 minimization”, Proceedings of the National Academy of Sciences, vol. 100, pp. 2197-2202, Mar. 2003.
Fletcher, R., “Semidefinite Matrix Constraints in Optimization”, SIAM Journal of Control and Optimization, vol. 23, pp. 493-513, 1985.
Hochreiter, Sepp., et al., “Monaural Separation and Classification of Mixed Signals: A support-vector regression Perspective”, 3rd International Conference on Independent Componenet Analysis and Blind Signal separation, San Diego, California, December 9-12, pp. 498-503, 2001.
Hofman, P.M., et al., “Bayesian Reconstruction of Sound Localization Cues from Responses to Random Spectra”, Biological Cybernetics, vol. 86(4), pp. 305-316, 2002.
Hofman, P.M., et al., “Relearning Sound Localization with New Ears”, Nature Neuroscience, vol. 1(5), pp. 417-421, 1998.
Jang, Gil-Jin, et al., “A Maximum Likelihood Approach to Single-Channel Source Separation”, Journal of Machine Learning Research, vol. 4., pp. 1365-1392, Dec. 2003.
King, A.J., et al., “Plasticity in the Neural Coding of Auditory Space in the Mammalian Brain”, Proc. National Academy of Science in the USA, vol. 97(22), pp. 11821-11828, 2000.
Knudsen, E.I., et al., “Mechanisms of Sound Localization in the Barn Owl”, Journal of Comparative Physiology, vol. 133, pp. 13-21, 1979.
Kukkarni, A., et al., “Role of Spectral Detail in Sound-Source Localization”, Nature, vol. 396(6713), pp. 747-749, 1998.
Lee, T.W., et al., “Blind Source Separation of More Sources than Mixtures Using Overcompete Representations”, IEEE Signal Processing Letters, vol. 4(5),pp. 87-90, 1999.
Lewicki M.S., et al., “Learning Overcomplete Representations”, Neural Computation, vol. 12(2), pp. 337-365, 2000.
Lewicki, M., et al., “Inferring sparse, Overcomplete Image Codes Using an Efficient Coding Framework”, In Advances in Neural Information Processing Systems 10, pp. 815-821, MIT Press, 1998.
Linkenhoker, B.A., et al., “Incremental Training Increases the Plasticity of the Auditory Space Map in Adult Barn Owls”, Nature, vol. 419(6904), pp. 293-296, 2002.
Olshausen, B.A., et al., “A new Window on Sound”, Nature Neuroscience, vol. 5, pp. 292-293, 2002.
Olshausen, B., et al., “Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images”, Nature, vol. 381, pp. 607-609, 1996.
Olshausen, B.A., et al., “Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?”, Vision Research, vol. 37(23), pp. 3311-3325, 1997.
Poggio, Tomaso., et al., “Computational Vision and Regularization Theory”, Nature, vol. 317(6035), pp. 314-319, 1985.
Rickard, Scott, et al., “DOA Estimation of Many W-disjoint Orthogonal Sources from Two Mixtures Using DUET”, In Proceedings of the 10th IEEE Workshop on Statistical Signal and Array Processing (SSAP2000), Pocono Manor, PA, pp. 311-314, Aug. 2000.
Riesenhuber, Maxmilian., et al., “Models of Object Recognition”, Nature Neuroscience, Supplement, vol. 2, pp. 1199-1204, 2000.
Roweis, Sam T., “One Microphone Source Separation”, Advances in Neural Information Processing Systems, pp. 793-799, MIT Press, 2001.
Shinn-Cunningham, B.G., “Models of Plasticity in Spatial Auditory Processing”, Audiology and Neuro-Otology, 2001, pp. 187-191, vol. 6(4).
Wenzel, E.M., et al., “Localization Using Nonindividualized Head-Related Transfer Functions”, Journal of the Acoustic Society of America, vol. 94(1), pp. 111-123, 1993.
Wightman, F.L., et al., “Headphone Simulation of Free-Field Listening, II: Psychophysical Validation”, Journal of the Acoustical Society of America, vol. 85(2), pp. 868-878, 1989.
Yost, Jr., W.A., et al., “A Simulated ‘cocktail party’ With Up to Three Sound Sources”, Percept Psychophys, vol. 58(7), pp. 1026-1036, 1996.
Zibulevsky, Michael, et al., “Blind Source Separation by Sparse Decomposition in a Signal Dictionary”, Neural Computation, vol. 13(4), pp. 863-882, Apr. 2001.
Pearlmutter Barak A.
Zador Anthony M.
Cold Spring Harbor Laboratory
Le Toan M.
McElheny Jr. Donald E.
National University of Ireland Maynooth
Ropes and Gray LLP
LandOfFree
Systems and methods for separating multiple sources using... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Systems and methods for separating multiple sources using..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Systems and methods for separating multiple sources using... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3900949