Analysis of an infant cry recognizer for the early identification of pathologies

Authors:
Orion F. Reyes-Galaviz;Antonio Verduzco;Emilio Arch-Tirado;Carlos A. Reyes-García
Affiliations:
Instituto Tecnológico de Apizaco, Apizaco, Tlaxcala, Mexico;Instituto Nacional de la Comunicación Humana, Mexico, D.F, Mexico;Instituto Nacional de la Comunicación Humana, Mexico, D.F, Mexico;Instituto Nacional de Astrofísica Óptica y Electrónica, Tonantzintla, Puebla, Mexico
Venue:
Nonlinear Speech Modeling and Applications
Year:
2005

Citing 2
Cited 1

Neural Networks in Computer Intelligence

Neural Networks in Computer Intelligence
Speech and Audio Signal Processing: Processing and Perception of Speech and Music

Speech and Audio Signal Processing: Processing and Perception of Speech and Music

Analysis of Infant Cry Through Weighted Linear Prediction Cepstral Coefficients and Probabilistic Neural Network

Journal of Medical Systems

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Abstract

This work presents the development and analysis of an automatic recognizer of infant cry, with the objective of classifying three classes, normal, hypo acoustics and asphyxia. We use acoustic feature extraction techniques like 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 for classification. We also use principal component analysis (PCA) in order to reduce vector's size and to improve training time. The complete infant cry database is represented by plain text vector files, which allows the files to be easily processed in any programming environment. The paper describes the design, implementation as well as experimentation processes, and the analysis of results of each type of experiment performed.