A Radial Basis Function Network Oriented for Infant Cry Classification

  • Sergio D. Cano Ortiz
  • Daniel I. Escobedo Beceiro
  • Taco Ekkel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)

Abstract

Several investigations around the world have been postulated that the infant cry can be utilized to asses the infant’s status and the use of artificial neural networks (ANN) has been one of the recent alternatives to classify cry signals. A radial basis function (RBF) network is implemented for infant cry classification in order to find out relevant aspects concerned with the presence of CNS diseases. First, an intelligent searching algorithm combined with a fast non-linear classification procedure is implemented, establishing the cry parameters which better match the physiological status previously defined for the six control groups used as input data. Finally the optimal acoustic parameter set is chosen in order to implement a new non-linear classifier based on a radial basis function network, an ANN-based procedure which classifies the cry units into a 2 categories, normal-or abnormal case. All the experiments were based on the physioacoustic model for cry production and the Golub’s muscle control model.

Keywords

Radial Basis Function Network Probabilistic Neural Network Component Classifier Exact Interpolation Bootstrap Aggregation 
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.

References

  1. 1.
    Bell, R.Q.: Contributions of human infants to caregiving and social interaction. In: Lewis, M., Rosenblum, L. (eds.) The effect of the infant on its caregiver, pp. 1–19. Wiley, New York (1974)Google Scholar
  2. 2.
    Breiman, L.: Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996)MATHMathSciNetGoogle Scholar
  3. 3.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995) ISBN 0198538642Google Scholar
  4. 4.
    Cano, S.D., Escobedo, D.: l.: El uso de los mapas auto-organizados de Kohonen en la clasificación de unidades de llanto infantil. In: Proceedings of the CYTED-AIRENE Project Meeting, Universidad Católica del Norte, Antofagasta, Chile, pp. 24–29 (1999)Google Scholar
  5. 5.
    Duda, R., Po, H., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001) ISBN 0-471-05669-3MATHGoogle Scholar
  6. 6.
    Golub, H., Corwin, M.: Infant cry: a clue to diagnosis. Pediatrics 69, 197–201 (1982)Google Scholar
  7. 7.
    Gustafson, G.E., Green, J.A.: On the importance of fundamental frequency in cry perception and infant development. Child Development 60 (August 1989)Google Scholar
  8. 8.
    Lester, B.M.: A biosocial model of infant crying. In: Leipsitt, L., Rovee, C. (eds.) Advances in Infancy Research, pp. 167–207. Ablex, Norwood (1984)Google Scholar
  9. 9.
    Schönweiler, R., Kaese, S., Möller, S., Rinscheid, A., Ptok, M.: Neuronal networks and selforganizing maps: new computer techniques in the acoustic evaluation of the infant cry. International Journal of Pediatric Otorhinolaryngology 38, 1–11 (1996)CrossRefGoogle Scholar
  10. 10.
    Wasz-Hockert, O., et al.: The infant cry: a spectrographic and auditory analysis. Clin. Devo Med. 29, 1–42 (1968)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sergio D. Cano Ortiz
    • 1
  • Daniel I. Escobedo Beceiro
    • 1
  • Taco Ekkel
    • 2
  1. 1.Group of Voice Processing, Faculty of Electrical EngineeringUniversity of OrienteSantiago de CubaCuba
  2. 2.Dept. of Computer ScienceUniversity of TwenteThe Netherlands

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