Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches
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Inclou dades d'ús des de 2022
Cita com:
hdl:2117/114468
Tipus de documentReport de recerca
Data publicació2017-11-12
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteTECNOLOGIAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ Y AUDIO (MINECO-TEC2015-69266-P)
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.
Descripció
Article publicat sense revisió per parells a Arxiv
CitacióRaboshchuk, G., Gómez, S., Peiró, A., Nadeu, C. "Automatic detection of alarm sounds in a noisy hospital environment using model and non-model based approaches". 2017.
URL repositori externhttps://arxiv.org/abs/1711.04351
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1711.04351(1).pdf | 471,2Kb | Visualitza/Obre |