Analysis of an Infant Cry Recognizer for the Early Identification of Pathologies

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.