Skip to main content
SpringerLink
Log in

Orthogonally-Constrained Extraction of Independent Non-Gaussian Component from Non-Gaussian Background Without ICA

  • Conference paper
  • First Online:
Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

Abstract

We propose a new algorithm for Independent Component Extraction that extracts one non-Gaussian component and is capable to exploit the non-Gaussianity of background signals without decomposing them into independent components. The algorithm is suitable for situations when the signal to be extracted is determined through initialization; it shows an extra stable convergence when the target component is dominant. In simulations, the proposed method is compared with Natural Gradient and One-unit FastICA, and it yields improved results in terms of the Signal-to-Interference ratio and the number of successful extractions.

This work was supported by The Czech Science Foundation through Project No. 17-00902S and partially supported by JSPS KAKENHI Grant Number 16H01735.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A particular variant of these algorithms (OGICE\(_\mathbf{w}\)) coincides with a method proposed earlier by Pham in [12], which was derived based on a simplified form of mutual information that is valid for Gaussian background.

  2. 2.

    Note that the separated sources by NG are not reordered after the separation, because the BSE problem is assumed to be resolved correctly only if the SOI appears in the assumed output channel.

References

  1. Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Proceedings of Neural Information Processing Systems, pp. 757–763 (1996)

    Google Scholar 

  2. Cardoso, J.F.: On the performance of orthogonal source separation algorithms. In: Proceedings of European Signal Processing Conference, pp. 776–779 (1994)

    Google Scholar 

  3. Comon, P.: Independent component analysis, a new concept? Sign. Process. 36, 287–314 (1994)

    Article  Google Scholar 

  4. Huber, P.J.: Projection pursuit. Ann. Statist. 13(2), 435–475 (1985)

    Article  MathSciNet  Google Scholar 

  5. Hyvärinen, A.: One-unit contrast functions for independent component analysis: a statistical analysis. In: Proceedings of the 1997 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing VII, pp. 388–397 (1997)

    Google Scholar 

  6. Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7), 1483–1492 (1997)

    Article  Google Scholar 

  7. Javidi, S., Mandic, D.P., Cichocki, A.: Complex blind source extraction from noisy mixtures using second-order statistics. IEEE Trans. Circuits Syst. I: Regul. Pap. 57(7), 1404–1416 (2010)

    Article  MathSciNet  Google Scholar 

  8. Kautský, V., Koldovský, Z., Tichavský, P.: Cramér-Rao-induced bound for interference-to-signal ratio achievable through non-gaussian independent component extraction. In: 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 94–97 (2017)

    Google Scholar 

  9. Kim, T., Attias, H.T., Lee, S.Y., Lee, T.W.: Blind source separation exploiting higher-order frequency dependencies. IEEE Trans. Audio Speech Lang. Process. 15(1), 70–79 (2007)

    Article  Google Scholar 

  10. Koldovský, Z., Tichavský, P.: Gradient algorithms for complex non-gaussian independent component/vector extraction (2018). ArXiv1803.10108

    Google Scholar 

  11. Koldovský, Z., Tichavský, P., Kautský, V.: Orthogonally constrained independent component extraction: Blind MPDR beamforming. In: Proceedings of European Signal Processing Conference, pp. 1195–1199 (2017)

    Google Scholar 

  12. Pham DTA: Blind partial separation of instantaneous mixtures of sources. In: Proceedings of International Conference on Independent Component Analysis and Signal Separation, pp. 868–875. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  13. 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 Trans. Speech Audio Process. 12(5), 530–538 (2004)

    Article  Google Scholar 

  14. Tichavský, P., Koldovský, Z., Oja, E.: Performance analysis of the FastICA algorithm and Cramér-Rao bounds for linear independent component analysis. IEEE Trans. Sign. Process. 54(4), 1189–1203 (2006)

    Article  Google Scholar 

  15. Tichavský, P., Koldovský, Z., Oja, E.: Corrections to “Performance analysis of the FastICA algorithm and Cramér Rao bounds for linear independent component analysis". IEEE Trans. Sign. Process. 56(4), 1715–1716 (2008)

    Article  Google Scholar 

  16. Van Trees, H.L.: Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory. Wiley, New York (2002)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Studentská 2, 461 17, Liberec, Czech Republic

    Zbyněk Koldovský

  2. The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod vodárenskou věží 4, P.O. Box 18, 182 08, Praha 8, Czech Republic

    Petr Tichavský

  3. Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino-shi, Tokyo, 191-0065, Japan

    Nobutaka Ono

Authors
  1. Zbyněk Koldovský
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petr Tichavský
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nobutaka Ono
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zbyněk Koldovský .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Koldovský, Z., Tichavský, P., Ono, N. (2018). Orthogonally-Constrained Extraction of Independent Non-Gaussian Component from Non-Gaussian Background Without ICA. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93764-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93763-2

  • Online ISBN: 978-3-319-93764-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation