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.

Authors: García-Gomez, Joaquin; Bautista-Durán, Marta; Gil-Pita, Roberto; Rosa-Zurera, Manuel
Affiliation: University of Alcala, Alcalá de Henares, Spain
AES Convention: 142 (May 2017) eBrief:308
Publication Date: May 11, 2017 Import into BibTeX
Subject: Posters: Analysis, Coding, and Hearing
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18684

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