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.
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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
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)
Breiman, L.: Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995) ISBN 0198538642
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)
Duda, R., Po, H., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001) ISBN 0-471-05669-3
Golub, H., Corwin, M.: Infant cry: a clue to diagnosis. Pediatrics 69, 197–201 (1982)
Gustafson, G.E., Green, J.A.: On the importance of fundamental frequency in cry perception and infant development. Child Development 60 (August 1989)
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)
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)
Wasz-Hockert, O., et al.: The infant cry: a spectrographic and auditory analysis. Clin. Devo Med. 29, 1–42 (1968)
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© 2004 Springer-Verlag Berlin Heidelberg
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Cano Ortiz, S.D., Escobedo Beceiro, D.I., Ekkel, T. (2004). A Radial Basis Function Network Oriented for Infant Cry Classification. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_46
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DOI: https://doi.org/10.1007/978-3-540-30463-0_46
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