Skip to main content

Separating Underdetermined Convolutive Speech Mixtures

  • Conference paper
Independent Component Analysis and Blind Signal Separation (ICA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3889))

Abstract

A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation techniques with binary time-frequency masking. In the proposed method, the number of source signals is not assumed to be known in advance and the number of sources is not limited to the number of microphones. Our approach needs only two microphones and the separated sounds are maintained as stereo signals.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2001)

    Book  Google Scholar 

  2. Roman, N., Wang, D.L., Brown, G.J.: Speech segregation based on sound localization. J. Acoust. Soc. Amer. 114, 2236–2252 (2003)

    Article  Google Scholar 

  3. Yilmaz, O., Rickard, S.: Blind separation of speech mixtures via time-frequency masking. IEEE Trans. Signal Processing 52, 1830–1847 (2004)

    Article  MathSciNet  Google Scholar 

  4. Wang, D.L., Brown, G.J.: Separation of speech from interfering sounds based on oscillatory correlation. IEEE Trans. Neural Networks 10, 684–697 (1999)

    Article  Google Scholar 

  5. Bregman, A.S.: Auditory Scene Analysis, 2nd edn. MIT Press, Cambridge (1990)

    Google Scholar 

  6. Jourjine, A., Rickard, S., Yilmaz, O.: Blind separation of disjoint orthogonal signals: Demixing N sources from 2 mixtures. In: Proc. ICASSP, pp. 2985–2988 (2000)

    Google Scholar 

  7. Roweis, S.: One microphone source separation. In: NIPS 2000, pp. 793–799 (2000)

    Google Scholar 

  8. Hu, G., Wang, D.L.: Monaural speech segregation based on pitch tracking and amplitude modulation. IEEE Trans. Neural Networks 15, 1135–1150 (2004)

    Article  MathSciNet  Google Scholar 

  9. Wang, D.L.: On ideal binary mask as the computational goal of auditory scene analysis. In: Divenyi, P. (ed.) Speech Separation by Humans and Machines, pp. 181–197. Kluwer, Norwell (2005)

    Chapter  Google Scholar 

  10. Araki, S., Makino, S., Sawada, H., Mukai, R.: Underdetermined blind separation of convolutive mixtures of speech with directivity pattern based mask and ICA. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 898–905. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  11. Kolossa, D., Orglmeister, R.: Nonlinear postprocessing for blind speech separation. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 832–839. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Pedersen, M.S., Wang, D.L., Larsen, J., Kjems, U.: Overcomplete blind source separation by combining ICA and binary time-frequency masking. In: Proceedings of the MLSP workshop, Mystic, CT, USA (2005)

    Google Scholar 

  13. Parra, L., Spence, C.: Convolutive blind separation of non-stationary sources. IEEE Trans. Speech and Audio Processing 8, 320–327 (2000)

    Article  Google Scholar 

  14. Büchler, M.C.: Algorithms for Sound Classification in Hearing Instruments. PhD thesis, Swiss Federal Institute of Technology, Zurich (2002)

    Google Scholar 

  15. Allen, J.B., Berkley, D.A.: Image method for efficiently simulating small-room acoustics. J. Acoust. Soc. Amer. 65, 943–950 (1979)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pedersen, M.S., Wang, D., Larsen, J., Kjems, U. (2006). Separating Underdetermined Convolutive Speech Mixtures. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_84

Download citation

  • DOI: https://doi.org/10.1007/11679363_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics