Identification of Reasons Behind Infant Crying Using Acoustic Signal Processing and Deep Neural Network for Neonatal Intensive Care Unit

摘要

The infants admitted in the neonatal intensive care unit (NICU) always need a hygienic environment and round the clock observations. Infants or the just born babies always express their physical and emotional needs through cry. Thus, the detection of the reasons behind the infant cry plays a vital role in monitoring the health of the babies in the NICU. In this paper, the authors have proposed a novel approach for detecting the reasons for Infant's cry. In the proposed approach the cry signal of the infant is captured, and from this signal, the unique set of features are extracted using MFCCs, LPCCs, and pitch. This set of features is used to differentiate the patters signals to recognize the reasons for the cry. The reasons for cry such as hunger, pain, sleep, and discomfort are used to represent different classes. The neural network multilayer classifier is designed to recognize the reasons for the cry using the standard dataset of infant cry. The proposed classifier can achieve accuracy of 93.24 from the combined features of MFCCs, LPCCs, and pitch.