The acoustic environment of a typical neonatal intensive care unit (NICU) is very rich and may contain a large number of different sounds, which come either from the equipment or from the human activities taking place in it. There exists a medical concern about the effect of that acoustical environment on preterm infants, since loud sounds or particular sounds may be harmful for their further neurological development. In this work, first of all, an initial description of the acoustic characteristics of the NICU has been carried out using a set of diverse recordings produced with microphones placed both inside and outside an incubator. Then, the work has focused on detection of the most relevant types of sounds. In this paper, after describing the recorded database and the acoustic environment, preliminary experiments for detection of the acoustic alarms of devices are reported. The proposed detection system is based on Deep Belief Networks (DBN). The experimental results show that the DBN-based system is able to achieve better results than a baseline GMM-based system.

Peer Reviewed