Proportionate Algorithms for Blind Source Separation

  • Michele Scarpiniti
  • Danilo Comminiello
  • Simone Scardapane
  • Raffaele Parisi
  • Aurelio Uncini
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 26)

Abstract

In this paper we propose an extension of time-domain Blind Source Separation algorithms by applying the well known proportionate and improved proportionate adaptive algorithms. These algorithms, known in the context of adaptive filtering, are able to use the sparseness of acoustic impulse responses of mixing environments and give better performances than standard algorithms. Some preliminary experimental results show the effectiveness of the proposed approach in terms of convergence speed.

Keywords

Blind Source Separation Independent Component Analysis Proportionate algorithms Improved proportionate 

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References

  1. 1.
    Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. John Wiley (2002)Google Scholar
  2. 2.
    Makino, S., Lee, T.W., Sawada, H.: Blind Speech Separation. Springer (2007)Google Scholar
  3. 3.
    Choi, S., Cichocki, A., Park, H.M., Lee, S.Y.: Blind source separation and independent component analysis: a review. Neural Information Processing - Letters and Reviews 6(1), 1–57 (2005)Google Scholar
  4. 4.
    Araki, S., Mukai, R., Makino, S., Nishikawa, T., Saruwatari, H.: The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Transactions on Speech and Audio Processing 11(2), 109–116 (2003)CrossRefGoogle Scholar
  5. 5.
    Duttweiler, D.L.: Proportionate normalized least-mean-square adaptation in echo cancelers. IEEE Transactions on Speech and Audio Processing 8, 508–518 (2000)CrossRefGoogle Scholar
  6. 6.
    Huang, Y., Benesty, J., Chen, J.: Acoustic MIMO Signal Processing. Springer (2006)Google Scholar
  7. 7.
    Benesty, J., Gay, S.L.: An improved PNLMS algorithm. In: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2002), pp. 1881–1884 (2002)Google Scholar
  8. 8.
    Torkkola, K.: Blind separation of convolved sources based on information maximization. In: Proc. of the 1996 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing, September 4-6, pp. 423–432 (1996)Google Scholar
  9. 9.
    Torkkola, K.: Blind deconvolution, information maximization and recursive filters. In: Proc. of 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1997), April 21-24, pp. 3301–3304 (1997)Google Scholar
  10. 10.
    Torkkola, K.: Blind separation of delayed sources based on information maximization. In: Proc. of 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1996), May 7-10, pp. 3509–3512 (1996)Google Scholar
  11. 11.
    Haykin, S.: Adaptive Filter Theory, 4th edn. Prentice-Hall (2001)Google Scholar
  12. 12.
    Ouedraogo, W.S.B., Jaidane, M., Souloumiac, A., Jutten, C.: Regularized gradient algorithm for non-negative independent component analysis. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), Prague, Czech Republic, May 22-27, pp. 2524–2527 (2011)Google Scholar
  13. 13.
    Boulmezaoud, T.Z., El Rhabi, M., Fenniri, H., Moreau, E.: On convolutive blind source separation in a noisy context and a total variation regularization. In: Proc. of IEEE Eleventh International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2010), Marrakech, June 20-23, pp. 1–5 (2010)Google Scholar
  14. 14.
    Masulli, F., Valentini, G.: Mutual information methods for evaluating dependence among outputs in learning machines. Technical Report TR-01-02, Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova (2001)Google Scholar
  15. 15.
    Torkkola, K.: Learning feature transforms is an easier problem than feature selection. In: Proc. of 16th International Conference on Pattern Recognition, August 11-15, pp. 104–107 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michele Scarpiniti
    • 1
  • Danilo Comminiello
    • 1
  • Simone Scardapane
    • 1
  • Raffaele Parisi
    • 1
  • Aurelio Uncini
    • 1
  1. 1.Department of Information Engineering, Electronics and Telecommunications (DIET)“Sapienza” University of RomeRomeItaly

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