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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, Postprint (published version)

The acoustic environment of a typical neonatal intensive care unit is very rich and may contain a large number of di erent sounds, which reflect the activities taking place in it. There exists a medical concern about the e ect of that acoustical environment on a preterm, since loud or particular sounds may be harmful for the preterm's further neurological development. In the reported work, an audio database of ten acoustic scenarios was recorded using two microphones, one inside and the other outside the incubator. Preliminary results of the acoustical analysis of sounds are presented along with the experiments on automatic detection of the presence of vocalizations., Peer Reviewed, Postprint (published version)