Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches
This addresses the challenge of monitoring critical alarms in a noisy hospital setting, but it appears incremental as it compares existing method types without introducing a fundamentally new technique.
The paper tackled the problem of automatically detecting alarm sounds in a noisy Neonatal Intensive Care Unit (NICU) environment by comparing non-model-based, model-based, and hybrid approaches, with performance assessed using a real-world audio database.
In the noisy acoustic environment of a Neonatal Intensive Care Unit (NICU) there is a variety of alarms, which are frequently triggered by the biomedical equipment. In this paper different approaches for automatic detection of those sound alarms are presented and compared: 1) a non-model-based approach that employs signal processing techniques; 2) a model-based approach based on neural networks; and 3) an approach that combines both non-model and model-based approaches. The performance of the developed detection systems that follow each of those approaches is assessed, analysed and compared both at the frame level and at the event level by using an audio database recorded in a real-world hospital environment.