A Cry Detection Method in Real Neonatal Intensive Care Units
The cry detection is very important in intelligent computerized systems to evaluate the wellbeing of neonates during their hospitalization periods. In addition, cry’s classification provides useful information (eg: tiredness, pain, hunger, …). Although several cry detection and characterization techniques can be found in the literature, the testing in real-life environments such as hospital Intensive Care Units is limited. In this article, first, we revise the background noise in Intensive Care Units,that may affect the cry detection algorithms’ result. Second, we implement a specific cry detection technique that is based on deep learning. Finally, we assess this method using audio samples recorded in a real neonatal intensive care unit and compare result to previous method of frequency different.
Keywords - Deep Learning, Neural Network, Cry detection, Fundamental Frequency.