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

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 appro...

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Published inarXiv.org
Main Authors Raboshchuk, Ganna, Sergi Gómez Quintana, Alex Peiró Lilja, Climent Nadeu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.11.2017
Subjects
Acoustic noise
Acoustics
Alarms
Neural networks
Signal processing
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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.
AbstractList 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.
Author Alex Peiró Lilja
Sergi Gómez Quintana
Climent Nadeu
Raboshchuk, Ganna
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SubjectTerms Acoustic noise
Acoustics
Alarms
Neural networks
Signal processing
Title Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches
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