Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2005-04-12
2005-04-12
McFadden, Susan (Department: 2655)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S223000, C381S094100, C381S094200, C381S066000, C381S071100
Reexamination Certificate
active
06879952
ABSTRACT:
Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.
REFERENCES:
patent: 5026051 (1991-06-01), Lowe et al.
patent: 5052685 (1991-10-01), Lowe et al.
patent: 5138660 (1992-08-01), Lowe et al.
patent: 5208786 (1993-05-01), Weinstein et al.
patent: 5272757 (1993-12-01), Scofield et al.
patent: 5291556 (1994-03-01), Gale
patent: 5436975 (1995-07-01), Lowe et al.
patent: 5448287 (1995-09-01), Hull
patent: 5473343 (1995-12-01), Kimmich et al.
patent: 5487113 (1996-01-01), Mark et al.
patent: 5534887 (1996-07-01), Bates et al.
patent: 5727122 (1998-03-01), Hosoda et al.
patent: 5768393 (1998-06-01), Mukojima et al.
patent: 5862229 (1999-01-01), Shimizu
patent: 5867654 (1999-02-01), Ludwig et al.
patent: 5872566 (1999-02-01), Bates et al.
patent: 5993318 (1999-11-01), Kousaki
patent: 6040831 (2000-03-01), Nishida
patent: 6046722 (2000-04-01), McKiel, Jr.
patent: 6081266 (2000-06-01), Sciammarella
patent: 6088031 (2000-07-01), Lee et al.
patent: 6097390 (2000-08-01), Marks
patent: 6097393 (2000-08-01), Prouty, IV et al.
patent: 6122381 (2000-09-01), Winterer
patent: 6185309 (2001-02-01), Attias
patent: 6647119 (2003-11-01), Slezak
Gauvain, J.-L.; Chin-Hui Lee, “Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains” Speech and Audio Processing, IEEE Transactions on ,vol.: 2, Issue: 2, Apr. 1994, pp.: 291-298.*
Amari S., Cichocki A. and Yang H.H. “A New Learning Algorithm for Blind Separation”. In D.S. Touretzky, M.C. Mozer and M.E. Hasselmo, editors,Advances in Neural Information Processing Systems, vol. 8, pp. 757-763. MIT Press, 1996.
H. Attias, “Independent Factor Analysis,”Neural Computation, vol. 11, no. 4, pp. 803-851, 1999.
H. Attias and C.E. Schreiner, “Blind Source Separation and Deconvolution: The Dynamic Component Analysis Algorithm,”Neural Computation, vol. 10, pp. 1373-1424, 1998.
M. Brandstein, “Explicit Speech Modeling for Distant-Talker Signal Acquisition,”preprint, 1998.
M. Brandstein, “On the Use of Explicit Speech Modeling in Microphone Array Applications.” InProceedings of ICASSP, pp. 3613-3616, 1998.
M. Brandstein and S. Griebel, “Nonlinear, Model-Based Microphone Array Speech Enhancement.” InTheory and Applications of Acoustic Signal Processing for Telecommunications, J. Benesty and S. Gay editors, Kluwer Academic Publishers, 2000.
J. Cardoso, “Blind Signal Separation: Statistical Principles.” InProceedings of the IEEE, vol. 90, no. 8, pp. 2009-2026, 1998.
J. Cardoso, “Infomax and Maximum Likelihood for Blind Source Separation.” InIEEE Signal Processing Letters, vol. 4, no. 4, pp. 112-114, 1997.
C. Jutten and J. Herault, “Blind Separation of Sources, Part I : An Adaptive Algorithm Based on Neuromimetic Architecture.” InSignal Processing, vol. 24, no. 1, pp. 1-10, 1991.
T.W. Lee, “Independent Component Analysis: Theory and Applications,” Kluwer Academic Publishers, 210 pages, 1998.
T.W. Lee, M. Girolami and T. Sejnowski, “Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources.” InNerual Computation, vol. 11, pp. 417-441, 1999.
B. Perlmutter and L. Parra, “A Context Sensitive Gereralization of ICA.” In M. Mozer, M. Jordan & T. Petsche, editors, Advances in Nerual Information Processing, vol. 9, pp. 613-619, Cambridge MA, 1997. MIT Press.
J. Platt and F. Faggin, “Networks for the Separation of Sources that are Superimpsoed and Delayed.” InProceedings of the Neural Information Processing Systems Conference, 1991, pp. 730-737, 1991.
E. Weinstein, M. Feder and A. Oppenheim, “Multi-Channel Signal Separation by Decorrelation.” InIEEE Transactions on Speech and Audio Processing, vol. 1, no. 4, pp. 405-413, 1993.
D. Yellin and E. Weinstein, “Criteria for Multichannel Signal Separation.” InIEEE Transactions on Signal Processing, vol. 42, no. 8, pp. 2158-2167, 1994.
M. Zibulevsky and B. Pearlmutter, “Blind Source Separation by Sparse Decomposition in a Signal Dictionary.” University of New Mexico Technical Report, No. CS99-1, 1999.
Acero Alejandro
Altschuler Steven J.
Wu Lani Fang
Kelly Joseph R.
McFadden Susan
Microsoft Corporation
Vo Huyen X.
Westman Champlin & Kelly P.A.
LandOfFree
Sound source separation using convolutional mixing and a... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Sound source separation using convolutional mixing and a..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sound source separation using convolutional mixing and a... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3411690