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

Author
Raboshchuk, G.; Gómez, S.; Peiró, A.; Nadeu, C.
Type of activity
Report
Date
2017-11-12
Project funding
Deep learning technologies for speech and audio processing
Repository
http://hdl.handle.net/2117/114468 Open in new window
URL
https://arxiv.org/abs/1711.04351 Open in new window
Abstract
Article publicat sense revisió per parells a Arxiv 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 a...
Citation
Raboshchuk, 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.
Keywords
Acoustic event detection, Matched filter, Neonatal intensive care, Neural networks, Sinusoidal detection
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group