Skip to content

Advertisement

Open Access

Underdetermined Blind Source Separation in Echoic Environments Using DESPRIT

EURASIP Journal on Advances in Signal Processing20062007:086484

https://doi.org/10.1155/2007/86484

Received: 1 October 2005

Accepted: 27 May 2006

Published: 10 September 2006

Abstract

The DUET blind source separation algorithm can demix an arbitrary number of speech signals using anechoic mixtures of the signals. DUET however is limited in that it relies upon source signals which are mixed in an anechoic environment and which are sufficiently sparse such that it is assumed that only one source is active at a given time frequency point. The DUET-ESPRIT (DESPRIT) blind source separation algorithm extends DUET to situations where sparsely echoic mixtures of an arbitrary number of sources overlap in time frequency. This paper outlines the development of the DESPRIT method and demonstrates its properties through various experiments conducted on synthetic and real world mixtures.

Keywords

Information TechnologyReal WorldQuantum InformationSource SignalSpeech Signal

[1234567891011121314151617181920212223242526]

Authors’ Affiliations

(1)
Sparse Signal Processing Group, University College Dublin, Belfield, Ireland

References

  1. Hyvarinen A, Karhunen J, Oja E: Independent Component Anaylsis, Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control. John Wiley & Sons, New York, NY, USA; 2001.Google Scholar
  2. Bell AJ, Sejnowski TJ: An information maximisation approach to blind separation and blind deconvolution. Neural Computation 1995,7(6):1129-1159. 10.1162/neco.1995.7.6.1129View ArticleGoogle Scholar
  3. Cichocki A, Amari S: Adaptive Blind Signal and Image Processing. John Wiley & Sons, New York, NY, USA; 2003.Google Scholar
  4. Yilmaz O, Rickard S: Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing 2004,52(7):1830-1846. 10.1109/TSP.2004.828896MathSciNetView ArticleGoogle Scholar
  5. Melia T, Rickard S, Fearon C: Histogram-based blind source separation of more sources than sensors using a DUET-ESPRIT technique. Proceedings of the 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, TurkeyGoogle Scholar
  6. Rickard S, Melia T, Fearon C: DESPRIT - histogram based blind source separation of more sources than sensors using subspace methods. Proceedings of the IEEE Workshop on Applications of Signal Processing in Audio and Acoustics, October 2005, New Paltz, NY, USA 5-8.Google Scholar
  7. Melia T, Rickard S, Fearon C: Extending the DUET blind source separation technique. Proceedings of Signal Processing with Adaptative Sparse Structured Representations Workshop (SPARS '05), November 2005, Rennes, FranceGoogle Scholar
  8. Balan R, Rosca J, Rickard S: Scalable non-square blind source separation in the presence of noise. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 293-296.Google Scholar
  9. Balan R, Rosca J: Sparse source separation using discrete prior models. Proceedings of Signal Processing with Adaptative Sparse Structured Representations Workshop (SPARS '05), November 2005, Rennes, FranceGoogle Scholar
  10. Li Y, Amari S-I, Cichocki A, Ho DWC, Xie S: Underdetermined blind source separation based on sparse representation. IEEE Transactions on Signal Processing 2006,54(2):423-437.View ArticleGoogle Scholar
  11. Saab R, Yilmaz O, McKeown M, Abugharbieb R: Underdetermined sparse blind source separation with delays. Proceedings of Signal Processing with Adaptative Sparse Structured Representations Workshop (SPARS '05), November 2005, Rennes, FranceGoogle Scholar
  12. O'Grady PD, Pearlmutter BA: Soft-LOST: EM on a mixture of oriented lines. Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA '04), September 2004, Granada, Spain 430-436.View ArticleGoogle Scholar
  13. Abrard F, Deville Y: A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources. Signal Processing 2005,85(7):1389-1403. 10.1016/j.sigpro.2005.02.010View ArticleMATHGoogle Scholar
  14. Georgiev P, Theis F, Cichocki A: Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Transactions on Neural Networks 2005,16(4):992-996. 10.1109/TNN.2005.849840View ArticleGoogle Scholar
  15. Blin A, Araki S, Makino S: A sparseness-mixing matrix estimation (SMME) solving the underdetermined BSS for convolutive mixtures. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 4: 85-88.Google Scholar
  16. Smaragdis P: Blind separation of convolved mixtures in the frequency domain. Proceedings of the International Workshop on Independence and Artificial Neural Networks, February 1998, Tenerife, SpainGoogle Scholar
  17. Torkkola K: Blind separation of convolved sources based on information maximisation. IEEE Workshop on Neural Networks and Signal Processing, September 1996, Kyoto, Japan 423-432.Google Scholar
  18. Murata N, Ikeda S, Ziehe A: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 2001,41(1–4):1-24.View ArticleMATHGoogle Scholar
  19. Sawada H, Mukai R, Araki S, Makino S: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Transactions on Speech and Audio Processing 2004,12(5):530-538. 10.1109/TSA.2004.832994View ArticleGoogle Scholar
  20. Lee T-W, Lewicki MS, Girolami M, Sejnowski TJ: Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters 1999,6(4):87-90. 10.1109/97.752062View ArticleGoogle Scholar
  21. Li Y, Cichocki A, Amari S-I: Analysis of sparse representation and blind source separation. Neural Computation 2004,16(6):1193-1234. 10.1162/089976604773717586View ArticleMATHGoogle Scholar
  22. Schmidt R: Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation 1986,34(3):276-280. 10.1109/TAP.1986.1143830View ArticleGoogle Scholar
  23. Roy R, Kailath T: ESPRIT - estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing 1989,37(7):984-995. 10.1109/29.32276View ArticleMATHGoogle Scholar
  24. Krim H, Viberg M: Two decades of array signal processing research: the parametric approach. IEEE Signal Processing Magazine 1996,13(4):67-94. 10.1109/79.526899View ArticleGoogle Scholar
  25. Ottersten B, Viberg M, Kailath T: Performance analysis of the total least squares ESPRIT algorithm. IEEE Transactions on Signal Processing 1991,39(5):1122-1135. 10.1109/78.80967View ArticleMATHGoogle Scholar
  26. Haardt M, Nossek JA: Unitary ESPRIT: how to obtain increased estimation accuracy with a reduced computational burden. IEEE Transactions on Signal Processing 1995,43(5):1232-1242. 10.1109/78.382406View ArticleGoogle Scholar

Copyright

© T. Melia and S. Rickard 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement