An investigation into classification of infant cries using modified signal processing methods

Infant cry is a biological signal through which an infant communicates with its care-giving environment. It also contains valuable information about the state of the infant. Infants produce this sound in response to a stimuli, which could be pain, discomfort, emotional need of attention, ailment, environmental factors or hunger/thirst. Signal processing methods that work well for adults are not adequate in the case of infants. In order to analyze the infant cry signals, these methods require some modifications. Signal processing methods such as short-time Fourier transform, auto-correlation and linear prediction analysis are modified and used. Features such as frame-energy and fundamental frequency are extracted from the cry signal. An Infant Cry and Causes (ICC) database especially collected for the study is used. Ground truth information about the fundamental frequency (F₀) is obtained using the spectrograms. The fluctuations in F₀ are examined using mean and standard deviation, for different causes of cry. The results about fluctuations in F₀ obtained from three different signal processing methods are compared. Objective is to classify the infant cry sounds into different categories of causes, based on the features derived from the infant cry signal. The results of the limited analysis are encouraging.