A Probability-Based Combination Method for Unsupervised Clustering with Application to Blind Source Separation

  • Julian Mathias Becker
  • Martin Spiertz
  • Volker Gnann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)

Abstract

Unsupervised clustering algorithms can be combined to improve the robustness and the quality of the results, e.g. in blind source separation. Before combining the results of these clustering methods the corresponding clusters have to be aligned, but usually it is not known which clusters of the employed methods correspond to each other. In this paper, we present a method to avoid this correspondence problem using probability theory. We also present an application of our method in blind source separation. Our approach is better expandable than other state-of-the-art separation algorithms while leading to slightly better results.

Keywords

Cluster Method Spectral Cluster Blind Source Separation Nonnegative Matrix Factorization Sound Event 
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.

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References

  1. 1.
    Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)MathSciNetMATHGoogle Scholar
  2. 2.
    Boulis, C., Ostendorf, M.: Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005)Google Scholar
  3. 3.
    Boulis, C., Ostendorf, M.: Combining Multiple Clustering Systems. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 63–74. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    FitzGerald, D., Cranitch, M., Coyle, E.: Extended nonnegative tensor factorisation models for musical sound source separation. In: Computational Intelligence and Neuroscience (2008)Google Scholar
  5. 5.
    Ozerov, A., Févotte, C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Transactions on Audio, Speech, and Language Processing 18(3), 550–563 (2010), http://www.irisa.fr/metiss/ozerov/demos.html CrossRefGoogle Scholar
  6. 6.
    Spiertz, M., Gnann, V.: Source-filter based clustering for monaural blind source separation. In: Proc. of International Conference on Digital Audio Effects DAFx, Como, Italy (2009)Google Scholar
  7. 7.
    Spiertz, M., Gnann, V.: Note clustering based on 2d source-filter modeling for underdetermined blind source separation. In: Proceedings of the AES 42nd International Conference on Semantic Audio, Ilmenau, Germany (July 2011)Google Scholar
  8. 8.
    Vincent, E., Gribonval, R., Fevotte, C.: Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech, and Language Processing 14(4), 1462–1469 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julian Mathias Becker
    • 1
  • Martin Spiertz
    • 1
  • Volker Gnann
    • 1
  1. 1.Institut für NachrichtentechnikRWTH Aachen UniversityAachenGermany

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