Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches

TL;DR

This paper compares signal processing, neural network, and combined approaches for automatic alarm sound detection in noisy NICU environments, evaluating their effectiveness at frame and event levels using real-world hospital data.

Contribution

It introduces and evaluates three different alarm detection methods, including a novel combination of non-model and model-based approaches, for improved accuracy in noisy hospital settings.

Findings

Combined approach outperforms individual methods in detection accuracy.

Neural network-based method shows robustness against noise.

Signal processing approach is computationally efficient but less accurate.

Abstract

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.