Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms

García-Gomez J, Bautista-Durán M, Gil-Pita R, Rosa-Zurera M. Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms. In: AES Convention 142. Audio Engineering Society; 2017. Paper 308. Available from: https://aes.org/publications/elibrary-page/?id=18684

@inproceedings{GarciaGomez2017_18684,
  author = {García-Gomez, Joaquin and Bautista-Durán, Marta and Gil-Pita, Roberto and Rosa-Zurera, Manuel},
  title = {{Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms}},
  booktitle = {AES Convention 142},
  note = {Paper 308},
  year = {2017},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18684}
}

TY  - CPAPER
TI  - Feature Selection for Real-Time Acoustic Drone Detection Using Genetic Algorithms
AU  - García-Gomez, Joaquin
AU  - Bautista-Durán, Marta
AU  - Gil-Pita, Roberto
AU  - Rosa-Zurera, Manuel
T2  - AES Convention 142
M1  - Paper 308
PY  - 2017
DA  - 2017/05/06
UR  - https://aes.org/publications/elibrary-page/?id=18684
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -