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http://dx.doi.org/10.12673/jant.2016.20.6.604

Drone Sound Identification and Classification by Harmonic Line Association Based Feature Vector Extraction  

Jeong, HyoungChan (Department of Aviation Industry and System Engineering, Inha University)
Lim, Wonho (Department of Aviation Industry and System Engineering, Inha University)
He, YuJing (Department of Electronic Engineering, Inha University)
Chang, KyungHi (Department of Electronic Engineering, Inha University)
Publication Information
Journal of Advanced Navigation Technology / v.20, no.6, 2016 , pp. 604-611 More about this Journal
Abstract
Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.
Keywords
Drone sound; Feature vector extraction; Classification; Harmonic line association;
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