Skip to main content

Audio Signal Blind Deconvolution Based on the Quotient Space Hierarchical Theory

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6954))

Abstract

The Time-Frequency Domain Blind Deconvolution is discussed based on the quotient space theory. The domain transformation [X w ] → [X n ] G  → [X] and the corresponding granular computing method is introduced to describe the hierarchical structure of deconvolution process, which converts the convolutive mixture of original time-domain signals into instantaneous mixtures in the quotient space. The experimental results show that the algorithm proposed in this paper achieves a relatively high separation quality.

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

Buying options

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aapo, H., Juha, K., Erkki, O.: Independent Component Analysis. John Wiley and Sons Inc., Chichester (2001)

    Google Scholar 

  2. Zhang, L., Zhang, B.: Theory and Applications of Problem Solving. Tsinghua University Press, Beijing (2007)

    Google Scholar 

  3. Zhang, L., Zhang, B.: A Quotient Space Approximation Model of Multiresolution Signal Analysis. Journal of Computer Science and Technology 20, 90–94 (2005)

    Article  MathSciNet  Google Scholar 

  4. Parra, L., Spence, C.: Convolutive blind source separation of nonstationary sources. IEEE Trans. on Speech Audio Process. 8(3), 320–327 (2000)

    Article  Google Scholar 

  5. Comon, P., Rota, L.: Blind separation of independent sources from convolutive mixtures. IEICE Trans. on Speech Audio Process. E86-A(3), 542–549 (2003)

    Google Scholar 

  6. Bingham, E., Hyvarinen, A.: A fast fixed-point algorithm for independent component analysis of complex valued signals. Int. J. Neural Systems 10, 1–8 (2000)

    Article  Google Scholar 

  7. Cardoso, J.F., Souloumiac, A.: Blind beamforming for nongaussian signals. Proc. Instit. Elec. Eng.-F, 362–370 (1993)

    Google Scholar 

  8. Li, H., Adali, T.: Gradient and fixed-point complex ICA algorithms based on kurtosis maximization. In: Proc. IEEE Workshop, pp. 85–90 (2006)

    Google Scholar 

  9. F’evotte, C., Gribonval, R., Vincent, E.: BSS EVAL toolbox user guide, http://www.irisa.fr/metiss/bsseval

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, C., Zhang, Y., Wu, Xp. (2011). Audio Signal Blind Deconvolution Based on the Quotient Space Hierarchical Theory. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24425-4_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24424-7

  • Online ISBN: 978-3-642-24425-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics