In this work we present the design of an automatic infant cry recognition system that classifies three different kinds of cries, which come from normal, deaf and asphyxiating infants, of ages from one day up to nine months old. The classification is done through a pattern classifier, where the crying waves are taken as the input patterns. We have experimented with patterns formed by vectors of Mel Frequency Cepstral Coefficients and Linear Prediction Coefficients. The acoustic feature vectors are then processed, to be classified in their corresponding type of cry, through an Input Delay Neural Network, trained by gradient descent with adaptive learning rate back propagation algorithm. To perform the experiments and to test the recognition system, we train the neural network with cries from randomly selected babies, and test it with a separate set of cries from babies selected only for testing. Here, we present the design and implementation of the complete system, as well as the results from some experiments, which in the presented case are up to 86 %
Cite as: Reyes-Galaviz, O.F., Reyes-Garcia, C.A. (2004) A system for the processing of infant cry to recognize pathologies in recently born babies with neural networks. Proc. 9th Conference on Speech and Computer (SPECOM 2004), 552-557
@inproceedings{reyesgalaviz04_specom,
author={Orion F. Reyes-Galaviz and Carlos Alberto Reyes-Garcia},
title={{A system for the processing of infant cry to recognize pathologies in recently born babies with neural networks}},
year=2004,
booktitle={Proc. 9th Conference on Speech and Computer (SPECOM 2004)},
pages={552--557}
}