Validity of the Independence Assumption for the Separation of Instantaneous and Convolutive Mixtures of Speech and Music Sources

  • Matthieu Puigt
  • Emmanuel Vincent
  • Yannick Deville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5441)


In this paper, we study the validity of the assumption that speech source signals exhibit lower dependency and therefore better separability with Independent Component Analysis algorithms than music sources. In particular, we investigate some dependency measures in the temporal and the time-frequency domains, resp. in the framework of instantaneous and convolutive mixtures. Moreover, we test several ICA methods, based on the above dependency measures, on the same source signals. We experimentally show that speech and music sources tend to have the same mean behaviour for excerpt durations above 20 ms, but music signals provide more spread dependency measures and SIR values. Lastly, we experimentally show that Gaussian nonstationary mutual information is better suited to audio signals than mutual information.


Mutual Information Independent Component Analysis Dependency Measure Independent Component Analysis Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience, New York (2001)CrossRefGoogle Scholar
  2. 2.
    Abrard, F., Deville, Y.: Blind separation of dependent sources using the TIme-Frequency Ratio Of Mixtures approach. In: Proc. Int. Symp. on Signal Processing and its Applications (ISSPA), pp. 81–84 (2003)Google Scholar
  3. 3.
    Smith, D., Lukasiak, J., Burnett, I.S.: An analysis of the limitations of blind signal separation application with speech. Signal Processing 86(2), 353–359 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Physical Review E 69(6) (2004); preprint 066138Google Scholar
  5. 5.
    Araki, S., Makino, S., Mukai, R., Nishikawa, T., Saruwatari, H.: The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Trans. on Speech and Audio Processing 11(2), 109–116 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Pham, D.T., Cardoso, J.-F.: Blind separation of instantaneous mixtures of nonstationary sources. IEEE Trans. on Signal Processing 49(9), 1837–1848 (2001)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Vincent, E., Gribonval, R., Plumbley, M.D.: Oracle estimators for the benchmarking of source separation algorithms. Signal Processing 87(8), 1933–1950 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Trans. on Neural Networks 10(3), 626–634 (1999)CrossRefGoogle Scholar
  9. 9.
    Mansour, A., Kawamoto, M., Ohnishi, N.: A survey of the performance indexes of ICA algorithms. In: Proc. of Int. Conf. on Modelling, Identification, and Control (2002)Google Scholar
  10. 10.
    Caiafa, C.F., Proto, A.N.: Separation of statistically dependent sources using an L 2-distance non-Gaussianity measure. Signal Processing 86, 3404–3420 (2006)CrossRefzbMATHGoogle Scholar
  11. 11.
    Deville, Y., Puigt, M.: Temporal and time-frequency correlation-based blind source separation methods. Part I: Determined and underdetermined linear instantaneous mixtures 87(3), 374–407 (2007)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matthieu Puigt
    • 1
  • Emmanuel Vincent
    • 2
  • Yannick Deville
    • 1
  1. 1.Laboratoire d’Astrophysique de Toulouse-TarbesUniversité de Toulouse, CNRSToulouseFrance
  2. 2.IRISA-INRIARennes cedexFrance

Personalised recommendations