Sound Classification in Hearing Aids by the Harmony Search Algorithm

  • Enrique Alexandre
  • Lucas Cuadra
  • Roberto Gil-Pita
Part of the Studies in Computational Intelligence book series (SCI, volume 191)


This chapter focuses on the application of the harmony search algorithms to the problem of selecting more appropriate features for sound classification in digital hearing aids. Implementing sound classification algorithms embedded in hearing aids is a very challenging task. Hearing aids have to work at very low clock frequency in order to minimize power consumption, and thus maximize battery life. This necessitates the reduction of computational load while maintaining a low error probability. Since the feature extraction process is one of the most time-consuming tasks, selecting a reduced number of appropriate features is essential, thus requiring low computational cost without degrading the operation. The music-inspired harmony-search (HS) algorithm allows for effectively searching adequate solutions to this strongly constrained problem. By starting with an initial set of 74 different sound-describing features, a number of experiments were carried out to test the performance of the proposed method. Results of the harmony search algorithm are compared to those reached by other widely used methods.


Sound Classification Feature Selection Hearing Aids 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Büchler, M.: Algorithms for sound classification in hearing instruments. PhD thesis, Swiss Federal institute of Technology, Zurich (2002)Google Scholar
  2. 2.
    Keidser, G.: The relationships between listening conditions and alternative amplifications schemes for multiple memory hearing aids. Ear Hear 16, 575–586 (1995)CrossRefGoogle Scholar
  3. 3.
    Keidser, G.: Selecting different amplification for different listening conditions. J. of the American Academy of Audiology 7, 92–104 (1996)Google Scholar
  4. 4.
    Alexandre, E., Cuadra, L., Álvarez, L., Rosa, M., López, F.: Two-layer automatic sound classification system for conversation enhancement. Integrated Computer Aided Engineering 15, 85–94 (2008)Google Scholar
  5. 5.
    Alexandre, E., Cuadra, L., Rosa, M., Francisco López, F.: Feature selection for sound classification in hearing aids through restricted search driven by genetic algorithms. IEEE Transactions on Audio, Speech and Language Processing 15, 2249–2256 (2007)CrossRefGoogle Scholar
  6. 6.
    Alexandre, E., Cuadra, L., Álvarez, L., Rosa-Zurera, M., López-Ferreras, F.: Automatic sound classification for improving speech intelligibility in hearing aids using a layered structure. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 306–313. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Alexandre, E., Cuadra, L., Rosa, M., López, F.: Speech/non-speech classification in hearing aids driven by tailored neural networks. In: Prasad, B., Prasanna, S.R.M. (eds.) Speech, Audio, Image and Biomedical Signal Processing Using Neural Networks. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Alexandre, E., Álvarez, L., Cuadra, L., Rosa, M., López, F.: Automatic sound classification algorithm for hearing aids using a hierarchical taxonomy. In: Bialostockiej, W.P., Skarbek, W., Dorbucki, A., Petrovsky, A. (eds.) New Trends in Audio and Video, Bialystok, vol. 1 (2006)Google Scholar
  9. 9.
    Cuadra, L., Alexandre, E., Álvarez, L., Rosa, M.: Reducing the computational cost for sound classification in hearing aids by selecting features via genetic algorithms with restricted search. In: Proceedings of IEEE ICALIP, Shanghai (2008)Google Scholar
  10. 10.
    Alexandre, E., Gil, R., Cuadra, L., Álvarez, L., Rosa, M.: Speech/music/noise classification in hearing aids using a two-layer classification system with MSE linear discriminants. In: Proceedings of European Signal Processing Conference, Switzerland (2008)Google Scholar
  11. 11.
    Amor, J., Alexandre, E., Gil, R., Álvarez, L., Huerta, E.: Music-inspired harmony search algorithm applied to feature selection for sound classification in hearing AIDS. In: Proceedings of AES 124th Convention Proceedings, Amsterdam (2008)Google Scholar
  12. 12.
    Alexandre, E., Cuadra, L., Álvarez, L., Utrilla, M.: Exploring the feasibility of a two-layer NN-based sound classifier for hearing aids. In: Proceedings of European Signal Processing Conference, Poznan (2007)Google Scholar
  13. 13.
    Alexandre, E., Cuadra, L., Álvarez, L., Rosa, M.: NN-based automatic sound classifier for digital hearing aids. In: Proceedings of IEEE WISP, Alcalá de Henares (2007)Google Scholar
  14. 14.
    Alexandre, E., Rosa, M., Cuadra, L., Gil, R.: Application of Fisher linear discriminant analysis to speech/music classification. In: Proceedings of AES 120th Convention, Paris (2006)Google Scholar
  15. 15.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2001)zbMATHGoogle Scholar
  16. 16.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2001)CrossRefGoogle Scholar
  17. 17.
    Geem, Z.W., Hwangbo, H.: Application of harmony search to multi-objective optimization for satellite heat pipe design. In: Proceedings of US-Korea Conference on Science, Technology & Entrepreneurship, Teaneck (2006)Google Scholar
  18. 18.
    Geem, Z.W., Tseng, C., Park, Y.: Harmony search for generalized orienteering problem: Best touring in china. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 741–750. Springer, Heidelberg (2005)Google Scholar
  19. 19.
    Geem, Z.W.: Optimal cost design of water distribution networks using harmony search. Engineering Optimization 38, 259–280 (2006)CrossRefGoogle Scholar
  20. 20.
    Geem, Z.W.: Optimal scheduling of multiple dam system using harmony search algorithm. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 316–323. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Geem, Z.W., Williams, J.C.: Harmony search and ecological optimization. International Journal of Energy and Environment 1, 150–154 (2007)Google Scholar
  22. 22.
    Scheirer, E., Slaney, M.: Construction and evaluation of a robust multifeature speech /music discriminator. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Washington, DC (1997)Google Scholar
  23. 23.
    Guaus, E., Batlle, E.: A non-linear rhythm-based style classification for broadcast speech-music discrimination. In: Proceedings of AES 116th Convention, Berlin (2004)Google Scholar
  24. 24.
    Lu, L., Zhang, H., Jiang, H.: Content analysis for audio classification and segmentation. IEEE Transactions on Speech and Audio Processing 10, 504–516 (2002)CrossRefGoogle Scholar
  25. 25.
    Davis, S., Mermelstein, P.: Experiments in syllable-based recognition of continuous speech. IEEE Transactions on Acoustics, Speech and Signal Processing 28, 357–366 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Enrique Alexandre
    • 1
  • Lucas Cuadra
    • 1
  • Roberto Gil-Pita
    • 1
  1. 1.Department of Signal Theory and CommunicationsUniversity of AlcaláAlcalá de HenaresSpain

Personalised recommendations