Infant cry classification to identify hypoacoustics and asphyxia with neural networks

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.