Waah: Infants Cry Classification of Physiological State Based on Audio Features

Crying is one of the means by which they express their emotional, psychological and physiological states. This paper investigates if audio features specifically the Mel Frequency Cepstral Coefficient (MFCC) & Linear Prediction Cepstral Coefficient (LPCC) are enough to classify the infants' physiological states such as hunger, pain and discomfort. To determine these physiological states, we recorded the cry of some infants who were immunized. The data for hunger and discomfort were recorded in the infants' house. The results showed that the audio features can classify an infant's physiological state. We used three classification algorithms, Decision Tree (J48), Neural Network and Support Vector Machine in developing the infant physiological model. To evaluate the performance of the infant physiological state model, Precision, Recall and F-measure were used as performance metrics. We found out that Decision Tree and Multilayer Perceptron performed better for the given dataset with correctly classified instances ranging from 87.64% to 90.80 with an overall kappa statistic ranging from 0.47 - 0.64.