Next Article in Journal
Embedding Piezoresistive Pressure Sensors to Obtain Online Pressure Profiles Inside Fiber Composite Laminates
Next Article in Special Issue
Optimization of Capacitive Acoustic Resonant Sensor Using Numerical Simulation and Design of Experiment
Previous Article in Journal
A Naked Eye Refractive Index Sensor with a Visible Multiple Peak Metamaterial Absorber
Previous Article in Special Issue
A Room Temperature Nitric Oxide Gas Sensor Based on a Copper-Ion-Doped Polyaniline/Tungsten Oxide Nanocomposite
Open AccessArticle

A Survey on the Feasibility of Sound Classification on Wireless Sensor Nodes

Ambient Intelligence Group, Saxion University of Applied Science, P.O. Box 70000, 7500KB Enschede, The Netherlands
Pervasive Systems Group, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Author to whom correspondence should be addressed.
Academic Editor: Gerhard Lindner
Sensors 2015, 15(4), 7462-7498;
Received: 17 December 2014 / Revised: 27 February 2015 / Accepted: 16 March 2015 / Published: 26 March 2015
(This article belongs to the Special Issue Acoustic Waveguide Sensors)
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. View Full-Text
Keywords: wireless sensor networks; sound; context awareness wireless sensor networks; sound; context awareness
MDPI and ACS Style

Salomons, E.L.; Havinga, P.J.M. A Survey on the Feasibility of Sound Classification on Wireless Sensor Nodes. Sensors 2015, 15, 7462-7498.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Back to TopTop