Improving the Performance of Acoustic Event Classification by Selecting and Combining Information Sources Using the Fuzzy Integral

  • Andrey Temko
  • Dušan Macho
  • Climent Nadeu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3869)


Acoustic events produced in meeting-room-like environments may carry information useful for perceptually aware interfaces. In this paper, we focus on the problem of combining different information sources at different structural levels for classifying human vocal-tract non-speech sounds. The Fuzzy Integral (FI) approach is used to fuse outputs of several classification systems, and feature selection and ranking are carried out based on the knowledge extracted from the Fuzzy Measure (FM). In the experiments with a limited set of training data, the FI-based decision-level fusion showed a classification performance which is much higher than the one from the best single classifier and can surpass the performance resulting from the integration at the feature-level by Support Vector Machines. Although only fusion of audio information sources is considered in this work, the conclusions may be extensible to the multi-modal case.


Support Vector Machine Hide Markov Model Information Source Support Vector Machine Classifier Automatic Speech Recognition 
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.
    Temko, A., Nadeu, C.: Meeting room acoustic event classification by support vector machines and variable-feature-set clustering. In: ICASSP 2005, Philadelphia (March 2005)Google Scholar
  2. 2.
    Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)zbMATHGoogle Scholar
  3. 3.
    Weston, J., Mukherjee, J., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature Selection for SVMs. In: Proc. of NIPS (2000)Google Scholar
  4. 4.
    Sugeno, M.: Theory of fuzzy integrals and its applications, PhD thesis, Tokyo Institute of Technology (1974)Google Scholar
  5. 5.
    Grabisch, M.: The Choquet integral as a linear interpolator. In: 10th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia (Italy), July 2004, pp. 373–378 (2004)Google Scholar
  6. 6.
    Grabisch, M.: Fuzzy integral in multi-criteria decision-making. Fuzzy Sets & Systems 69, 279–298 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Kuncheva, L.: ‘Fuzzy’ vs ‘Non-fuzzy’ in combining classifiers designed by boosting. IEEE Transactions on Fuzzy Systems 11(6), 729–741 (2003)CrossRefGoogle Scholar
  8. 8.
    Chang, S., Greenberg, S.: Syllable-proximity evaluation in automatic speech recognition using fuzzy measures and a fuzzy integral. In: Proc. of the 12th IEEE Fuzzy Systems Conf., pp. 828–833 (2003)Google Scholar
  9. 9.
    Grabisch, M.: A new algorithm for identifying fuzzy measures and its application to pattern recognition. In: Proc. of 4th IEEE Int. Conf. on Fuzzy Systems, Yokohama, Japan, pp. 145–150 (1995)Google Scholar
  10. 10.
    Wu, Y., Chang, E., Chang, K., Smith, J.: Optimal Multimodal Fusion for Multimedia Data Analysis. In: Proc. ACM Int. Conf. on Multimedia, New York, pp. 572–579 (October 2004)Google Scholar
  11. 11.
    Kuncheva, L.: Combining classifiers: Soft computing solutions. Lecture Notes in Pattern Recognition, pp. 427–452. World Scientific Publishing Co, Singapore (2001)Google Scholar
  12. 12.
    Marichal, J.-L.: Behavioral analysis of aggregation in multicriteria decision aid, Preferences and Decisions under Incomplete Knowledge. Studies in Fuzziness and Soft Computing 51, 153–178 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Marichal, J.-L.: Entropy of discrete Choquet capacities. European Journal of Operational Research 137(3), 612–624 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kojadinovic, I., Marichal, J.-L., Roubens, M.: An axiomatic approach to the definition of the entropy of a discrete choquet capacity. In: 9th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2002), Annecy (France), pp. 763–768 (2002)Google Scholar
  15. 15.
    Evaluation Packages for the First CHIL Evaluation Campaign. In: CHIL project Deliverable D7.4 (Mar 2005) Available at,
  16. 16.
    Kuncheva, L.: Combining Pattern Classifiers. John Wiley & Sons, Inc, Chichester (2004)CrossRefzbMATHGoogle Scholar
  17. 17.
    Mikenina, L., Zimmermann, H.: Improved feature selection and classification by the 2-additive fuzzy measure. Fuzzy Sets and Systems 107(2), 197–218 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Nadeu, C., Hernando, J., Gorricho, M.: On the decorrelation of filter-bank energies in speech recognition. In: Proc. Eurospeech 1995, pp. 1381–1384 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrey Temko
    • 1
  • Dušan Macho
    • 1
  • Climent Nadeu
    • 1
  1. 1.TALP Research CenterUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations