Improving Baby Caring with Automatic Infant Cry Recognition

  • Sandra E. Barajas-Montiel
  • Carlos A. Reyes-García
  • Emilio Arch-Tirado
  • Mario Mandujano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4061)


Babies are human beings who cannot satisfy their necessities by themselves, they completely depend of cares and attentions by adults. The cry is the natural media babies use to express their needs. Several studies have demonstrated that cry is a useful tool to determine the different emotional and physiological states from an infant, and in addition to make medical diagnoses of diseases related to the central nervous system. This work presents the analysis and extraction of characteristics from infant crying for its automatic classification with Support Vector Machines. Several classification tasks were done, working in the identification of pain, hunger, and deafness levels with results of up to 96 % of correct classification. Besides some results, we show the implementation and experimentation done.


Support Vector Machine Hearing Loss Radial Basis Function Speaker Verification Linear Prediction Coefficient 
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.
    Wasz-Hockert, O., Partanen, T., Vuorenkoski, V., Valanne, E., Michelsson, K.: The Identification of Some Specific Meanings in Infant Vocalization. Experiencia 20, 154–156 (1964)CrossRefGoogle Scholar
  2. 2.
    Cano-Ortiz, S.D., Escobedo-Becerro, D.I.: Clasificación de Unidades de Llanto Infantil Mediante el Mapa Auto-Organizado de Koheen. I Taller AIRENE sobre Reconocimiento de Patrones con Redes Neuronales, Universidad Católica del Norte, Chile, pp. 24–29 (1999)Google Scholar
  3. 3.
    Reyes, J., Reyes, C.A.: Mel-frequency Cepstrum Coefficients Extraction from Infant Cry for Classification of Normal and Pathological Cry whit Feed-Forward Neural Networks. In: Proc. of ESANN, Bruges, Belgium (2003)Google Scholar
  4. 4.
    Ortiz, S.D.C., Escobedo Beceiro, D.I., Ekkel2, T.: A Radial Basis Function Network Oriented for Infant Cry Classification. In: Proc. Of 9th Iberoamerican Congress on Pattern Recognition, Puebla, Mexico (2004)Google Scholar
  5. 5.
    Suaste, I., Reyes, O.F., Diaz, A., Reyes, C.A.: Implementation of a Linguistic Fuzzy Relational Neural Network for Detecting Pathologies by Infant Cry Recognition. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 953–962. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Petroni, M., Malowany, A.S., Johnston, C.C., Stevens, B.J.: Identification of Pain from Infant Cry Vocalizations Using Artificial Neural Networks. In: Proc. of SPIE, vol. 2492, pp. 729–738 (1995)Google Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 1–25 (1995)Google Scholar
  8. 8.
    Wan, V., Campbell, W.M.: Support Vector Machines for Speaker Verification and Identification. In: IEEE International Workshop on Neural Networks for Signal Processing, Sydney, Australia (2000)Google Scholar
  9. 9.
    Livinson, S.E., Roe, D.B.: A Perspective on Speech Recognition. IEEE Communications Magazine, 28–34 (1990)Google Scholar
  10. 10.
    Orosco, G.J., Reyes, C.A.: Mel-Frequency Cepstrum Coefficients Extraction from Infant Cry for Classification of Normal and Pathological Cry with Feed-Forward Neural Networks. In: Proc. International Joint Conference on Neural Networks, Portland, Oregon, USA, pp. 3140–3145 (2003)Google Scholar
  11. 11.
    Reyes, O., Reyes, C.A.: Clasificación de Llanto de Bebés para Identificación de Hipoacusia y Asfixia por medio de Redes Neuronales. In: de la Aniel. (ed.) Proc. of the II Congreso Internacional de Informática y Computación Zacatecas, México, pp. 20–24 (2003)Google Scholar
  12. 12.
    Osun, E., Freund, R., Girosi, F.: Support Vector Machines: Training and Applications, Massachusetts Institute of Technology, Cambridge, MA, USA (1997)Google Scholar
  13. 13.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  14. 14.
    Fran, V., Hlavac, V.: Statistical Pattern Recognition Toolbox, Czech Technical University Prague (1999)Google Scholar
  15. 15.
    Boersma, P., Weenink, D.: Praat v 4.0.8: A System for Doing Phonetics by Computer. Institute of Phonetic Sciences of the University of Amsterdam (2002)Google Scholar
  16. 16.
    Yoshinaga-Itano,: Quoted in The High Cost Of Hearing Lost; What Our Publics Need to Know Donald Radcliffe. The Hearing Journal 51(5) (May 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sandra E. Barajas-Montiel
    • 1
  • Carlos A. Reyes-García
    • 1
  • Emilio Arch-Tirado
    • 2
  • Mario Mandujano
    • 2
    • 3
  1. 1.Instituto Nacional de Astrofisica Optica y ElectronicaTonantzintla, PueblaMexico
  2. 2.Instituto Nacional de Rehabilitación 
  3. 3.UAM-Xochimilco 

Personalised recommendations