Blind separation of non-negative source signals using multiplicative updates and subspace projection

Signal Processing

Volumn 90, Issue 10, 2010, Pages 2877-2890

  Bertrand, Alexander ^a   Moonen, Marc ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Multi channel signal processing; Multiplicative updating; Non negative blind source separation; Non negative independent component analysis (NICA)

Indexed keywords

BATCH MODES; BLIND SEPARATION; DATA SAMPLE; GRADIENT BASED; LEARNING RATES; LINEAR MIXTURES; MULTI-CHANNEL; MULTI-CHANNEL SIGNAL PROCESSING; MULTIPLICATIVE UPDATES; NON-NEGATIVE INDEPENDENT COMPONENT ANALYSIS; NON-NEGATIVITY; NOVEL ALGORITHM; SECOND ORDER STATISTICS; SLIDING WINDOW; SOURCE SIGNALS; SUBSPACE PROJECTION; UNMIXING;

INDEPENDENT COMPONENT ANALYSIS; INTERFERENCE SUPPRESSION; LEARNING ALGORITHMS; SIGNAL ANALYSIS; SIGNAL PROCESSING;

BLIND SOURCE SEPARATION;

EID: 77955303756     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2010.04.015     Document Type: Article

References (22)

1. P. Comon, and C. Jutten Handbook of Blind Source Separation 2010 Academic Press New York
2. A. Cichocki, and S. Amari Adaptive Blind Signal and Image Processing 2002 J. Wiley New York
3. E. Oja, M. Plumbley, Blind separation of positive sources using non-negative PCA, in: Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, April 2003.
4. A. Bertrand, M. Moonen, Energy-based multi-speaker voice activity detection with an ad hoc microphone array, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, ICASSP 2010, March 2010, pp. 8588.
5. P. Pauca, R. Plemmons, M. Giffin, K. Hamada, Unmixing spectral data for space objects using independent component analysis and nonnegative matrix factorization, in: Proceedings Amos Technical Conference, 2004.
6. J. Nascimento, and J. Dias Does independent component analysis play a role in unmixing hyperspectral data? IEEE Transactions on Geoscience and Remote Sensing 43 January 2005 175 187
7. L. Parra, C. Spence, P. Sajda, A. Ziehe, and K.-R. Müller. Unmixing hyperspectral data, in: T. Leen, T. Dietterich, V. Tresp (Eds.), Advances in Neural Information Processing Systems, vol. 12, MIT Press, 2000, pp. 942948
8. D. Nuzillard, and J.-M. Nuzillard Application of blind source separation to 1-D and 2-D nuclear magnetic resonance spectroscopy IEEE Signal Processing Letters 5 August 1998 209 211
9. R.C. Henry Multivariate receptor models-current practice and future trends Chemometrics and Intelligent Laboratory Systems 60 12 2002 43 48
10. S. Abdallah, M. Plumbley, Polyphonic transcription by non-negative sparse coding of power spectra, in: Proceedings of the International Conference on Music Information Retrieval (ISMIR), Barcelona, Spain, October 2004, pp. 318325.
11. D.D. Lee, and H.S. Seung Learning the parts of objects by non-negative matrix factorization Nature 401 October 1999 788 791
12. D.D. Lee, H.S. Seung. Algorithms for non-negative matrix factorization, in: Advances in Neural Information Processing Systems, vol. 13, 2001, pp. 556562.
13. F.Y. Wang, C.Y. Chi, T.H. Chan, Y. Wang, Blind separation of positive dependent sources by non-negative least-correlated component analysis, in: Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, September 2006, pp. 7378.
14. T.-H. Chan, W.-K. Ma, C.-Y. Chi, Y. Wang, Blind separation of non-negative sources by convex analysis: effective method using linear programming, in: IEEE International Conference on Speech and Signal Processing, 2008, ICASSP 2008, April 2008, pp. 34933496.
15. Z. Yang, and J. Laaksonen Multiplicative updates for non-negative projections Neurocomputing 71 13 2007 363 373 (Dedicated Hardware Architectures for Intelligent Systems; Advances on Neural Networks for Speech and Audio Processing)
16. M. Plumbley Conditions for nonnegative independent component analysis IEEE Signal Processing Letters 9 June 2002 177 180
17. M. Ye Global convergence analysis of a discrete time nonnegative ICA algorithm IEEE Transactions on Neural Networks 17 January 2006 253 256 (Pubitemid 43263958)
18. S. Amari, S. Douglas, Why natural gradient? in: IEEE International Conference on Acoustics, Speech and Signal Processing, 1998, ICASSP 1998, vol. 2, May 1998, pp. 12131216.
19. J. Boyle, R. Dykstra, A method for finding projections onto the intersection of convex sets in Hilbert spaces, in: Lecture Notes in Statistics, vol. 37, 1986, pp. 2847.
20. J. Nocedal, S. Wright, Numerical Optimization, Springer, Berlin, 1999.
21. R. Badeau, G. Richard, and B. David Sliding window adaptive SVD algorithms IEEE Transactions on Signal Processing 52 January 2004
22. P. Strobach Sliding window adaptive SVD using the unsymmetric householder partial compressor Signal Processing 90 1 2010 352 362