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Author Salomons, Etto L. ♦ Havinga, Paul J. M.
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Technology ♦ Medicine & health
Abstract Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem.
Description Country affiliation: Netherlands
Author Affiliation: Salomons EL ( Ambient Intelligence Group, Saxion University of Applied Science, P.O. Box 70000, 7500KB Enschede, The Netherlands. e.l.salomons@saxion.nl.); Havinga PJ ( Pervasive Systems Group, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands. p.j.m.havinga@utwente.nl.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-03-26
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 15
Issue Number 4


Source: WHO-Global Index Medicus