TRINICON: A versatile framework for multichannel blind signal processing

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 3, Issue , 2004, Pages

  Buchner, Herbert ^a   Aichner, Robert ^a   Kellermann, Walter ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INVERSE PROBLEMS; MATRIX ALGEBRA; SENSORS; SPEECH RECOGNITION; THEOREM PROVING; TIME DOMAIN ANALYSIS;

BLOCK PROCESSING; MULTIVARIATE MODELS; OUTPUT SIGNALS; SENSOR SIGNALS;

BLIND SOURCE SEPARATION;

EID: 4544356482     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. M. Miyoshi and Y. Kaneda, "Inverse filtering of room acoustics," IEEE Trans. on Acoust., Speech and Audio Processing, vol 36, no. 2, pp. 145-152, Feb. 1988.
2. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, Wiley & Sons, Inc., New York, 2001.
3. H.-C. Wu and J. C. Principe,"Simultaneous diagonalization in the frequency domain (SDIF) for source separation," in Proc. ICA, pp. 245-250, 1999.
4. C.L. Fancourt and L. Parra, "The coherence function in blind source separation of convolutive mixtures of non-stationary signals," in Proc. NNSP, 2001.
5. S. Amari, T.-P. Chen, and A. Cichocki, "Nonholonomic orthogonal learning algorithms for blind source separation," Neural Computation, vol. 12, no. 6, pp. 1463-1484, 2000.
6. S. Amari et al., "Multichannel blind deconvolution and equalization using the natural gradient," Proc. IEEE Int. Workshop on Signal Processing Advances in Wireless Communications, pp. 101-107, 1997.
7. S. Choi et al., "Natural gradient learning with a nonholonomic constraint for blind deconvolution of multiple channels," Proc. Int. Symp ICA, pp. 371-376, 1999.
8. B.W. Gillespie and L. Atlas, "Strategies for improving audible quality and speech recognition accuracy of reverberant speech," Proc. IEEE ICASSP, 2003.
9. H. Buchner, R. Aichner, and W. Kellermann, "Blind Source Separation for Convolutive Mixtures Exploiting Nongaussianity, Nonwhiteness, and Nonstationarity," Proc. Int. Workshop on Acoustic Echo and Noise Control, 2003.
10. H. Buchner, R. Aichner, and W. Kellermann, "A generalization of a class of blind source separation algorithms for convolutive mixtures," Proc. Int. Symp ICA, 2003.
11. H. Buchner, R. Aichner, and W. Kellermann, "Blind Source Separation for Convolutive Mixtures: A Unified Treatment," in Y. Huang and J. Benesty (eds.), Audio Signal Processing for Next-Generation Multimedia Communication Systems, Kluwer Academic Publishers, Boston, to appear 2004.
12. T.M. Cover and J.A. Thomas, Elements of Information Theory, Wiley & Sons, New York, 1991.
13. M. Kawamoto, K. Matsuoka, and N. Ohnishi, "A method of blind separation for convolved non-stationary signals," Neurocomputing, vol. 22, pp. 157-171, 1998.
14. T. Nishikawa, H. Saruwatari, and K. Shikano, "Comparison of time-domain ICA, frequency-domain ICA and multistage ICA for blind source separation," in Proc. European Signal Processing Conference, vol. 2, pp. 15-18, Sep. 2002.
15. R. Aichner et al., "Time-domain blind source separation of non-stationary convolved signals with utilization of geometric beamforming," in Proc. NNSP, 2002.
16. H. Brehm and W. Stammler, "Description and generation of spherically invariant speech-model signals," Signal Processing vol. 12, pp. 119-141, 1987.