Evolutionary-Neural System to Classify Infant Cry Units for Pathologies Identification in Recently Born Babies

This work presents an infant cry automatic recognizer development, with the objective of classifying two kinds of infant cries, normal and pathological, from recently born babies. Extraction of acoustic features is used such as MFCC (Mel Frequency Cepstral Coefficients), obtained from Infant Cry Units sound waves, and a genetic feature selection system combined with a feed forward input delay neural network, trained by adaptive learning rate back-propagation. For the experiments, recordings from Cuban and Mexican babies are used, classifying normal and pathological cry in three different experiments; Cuban babies, Mexican Babies, and Cuban & Mexican babies. It is also shown a comparison between a simple traditional feed-forward neural network and another complemented with the proposed genetic feature selection system, to reduce the feature input vectors. In this paper the whole process is described; in which the acoustic features extraction is included, the hybrid system design, implementation, training and testing. The results from some experiments are also shown, in which the infant cry recognition rate obtained is of up to 100% using our genetic system.