Abstract
This work presents the development of an automatic recognizer of infant cry, with the objective of classifying three kinds of cry, normal, hypoacoustic and asphyxia. We use acoustic characteristics extraction techniques like LPC and MFCC, for the acoustic processing of the cry’s sound wave, and a Feed Forward Input Delay neural network with training based on Gradient Descent with Adaptive Back-Propagation. We describe the whole process, and we also show the results of some experiments, in which we obtain up to 98.67% precision.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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 (February 2002)
Cano, S.D., Escobedo, D.I., Coello, E.: El Uso de los Mapas Auto- Organizados de Kohonen en la ClasificaciUf3n de Unidades de Llanto Infantil, Grupo de Procesamiento de Voz, 1er Taller AIRENE, Universidad Catolica del Norte, Chile, pp. 24–29 (1999)
Petroni, M., Malowany, A.S., Celeste Johnston, C., Stevens, B.J.: Identification of pain from infant cry vocalizations using artificial neural networks (ANNs). In: The International Society for Optical Engineering, vol. 2492. Part two of two. Paper #: 2492-79 (1995)
Ekkel, T.: Neural Network-Based Classification of Cries from Infants Suffering from Hypoxia-Related CNS Damage, Master Thesis. University of Twente, The Netherlands (2002)
Orozco GarcUeda, J., Reyes GarcUeda, C.A.: Mel-Frequency Cepstrum Coefficients Extraction from Infant Cry for Classification of Normal and Pathological Cry with Feed-forward Neural Networks. In: ESANN 2003, Bruges, Belgium (2003)
Gold, B., Morgan, N.: Speech and Audio Signal Processing. Processing and Perception of Speech and Music. John Wiley & Sons, Inc., Chichester (2000)
Wikipedia: The Free Encyclopedia, http://www.wikipedia.org/wiki/Linear_prediction
Markel, J.D., Gray, A.H.: Linear prediction of speech. Springer, New York (1976)
DARPA Neural Network Study, AFCEA International Press, p. 60 (1988)
Manual Neural Network Toolbox, Matlab V.6.0.8, Developed by Math Woks, Inc.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.: Phoneme Recognition Using Time-Delay Neural Networks. IEEE Transactions on Acoustics, Speech and Signal Processing 37(3), 328–339 (1989)
Fu, L.: Neural Networks in Computer Intelligence. McGraw-Hill International Editions. Computer Science Series (1994)
Tebelskis, J.: Speech Recognition Using Neural Networks, PhD Dissertation, Carnegie Mellon University (1955)
Plagianakos*, V.P., Vrahatis*, M.N., Magoulas**, G.D.: Nonmonotone Methods for Backpropagation Training with Adaptive Learning Rate, University of Patras* Patras, Greece; University of Athens** Athens, Greece
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reyes Galaviz, O.F., Reyes Garcia, C.A. (2004). Infant Cry Classification to Identify Hypoacoustics and Asphyxia with Neural Networks. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-24694-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
Online ISBN: 978-3-540-24694-7
eBook Packages: Springer Book Archive