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Drones are taking off in a big way, but people sometimes use them in order to invade the privacy of others or to bypass the security systems, making their detection an actual issue. The objective of the proposed system is to design real-time acoustic drone detectors, able to distinguish them from objects that can be acoustically similar. A set of features related to the propeller sounds have been extracted, and genetic algorithms have been used to select the best subset. The classification error achieved with 30 features is below 13%, making feasible the real-time implementation of the proposed system.
Author (s): García-Gomez, Joaquin; Bautista-Durán, Marta; Gil-Pita, Roberto; Rosa-Zurera, Manuel
Affiliation:
University of Alcala, Alcalá de Henares, Spain
(See document for exact affiliation information.)
AES Convention: 142
Paper Number:308
Publication Date:
2017-05-06
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Session subject:
Posters: Analysis, Coding, and Hearing
Permalink: https://aes2.org/publications/elibrary-page/?id=18684
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García-Gomez, Joaquin; Bautista-Durán, Marta; Gil-Pita, Roberto; Rosa-Zurera, Manuel; 2017; Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms [PDF]; University of Alcala, Alcalá de Henares, Spain; Paper 308; Available from: https://aes2.org/publications/elibrary-page/?id=18684
García-Gomez, Joaquin; Bautista-Durán, Marta; Gil-Pita, Roberto; Rosa-Zurera, Manuel; Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms [PDF]; University of Alcala, Alcalá de Henares, Spain; Paper 308; 2017 Available: https://aes2.org/publications/elibrary-page/?id=18684
@article{garcía-gomez2017feature,
author={garcía-gomez joaquin and bautista-durán marta and gil-pita roberto and rosa-zurera manuel},
journal={journal of the audio engineering society},
title={feature selection for real-time acoustic drone detection using genetic algorithms},
year={2017},
number={308},
month={may},}