Acoustic Features Analysis for Recognition of Normal and Hypoacustic Infant Cry Based on Neural Networks

Authors:
José Orozco García;Carlos A. García
Affiliations:
Instituto Nacional de Astrofísica Óptica y Electrónica (INAOE), Tonantzintla, México;Instituto Nacional de Astrofísica Óptica y Electrónica (INAOE), Tonantzintla, México
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

Citing 4
Cited 4

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Abstract

This work presents the development of an automatic recognition system of infant cry, with the objective to classify two types of cry: normal and pathological cry from deaf babies. In this study, we used acoustic characteristics obtained by the Mel-Frequency Cepstrum and Lineal Prediction Coding techniques and as a classifier a feed-forward neural network that was trained with several learning methods, resulting better the Scaled Conjugate Gradient algorithm. Current results are shown, which, up to the moment, are very encouraging with an accuracy up to 97.43%.