Mel-frequency cepstrum coefficients extraction from infant cry for classification of normal and pathological cry with feed-forward neural networks

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 technique and as a classifier a feedforward neural network that was trained with several learning methods, resulting in a better scaled conjugate gradient algorithm. Current results are shown, which, at the moment, are very encouraging with an accuracy up to 97.43%.