Automatic methods for infant cry classification

Studies have shown that newborns are crying differently depending on their need: hunger, tiredness, discomfort, eructation, pain, and so on. Skilled persons such as pediatricians can distinguish between different types of newborn cries and consequently estimate the baby's need by using the sounds and the gestures produced by the baby. However, this is a real problem for the unskilled parents who would like to answer promptly to their baby needs. Recently, research work has been invested into developing automated methods to detect the baby's need using only a short audio recording of his/her live cry. In this paper we report the initial results we obtained by applying various methods that were successful in speaker and language recognition to the task of baby cry recognition. The results are promising: 70% accuracy on a dataset with 39 babies and 5 types of cry.