Distinguishing drone types based on acoustic wave by IoT device

Nappaphol Siriphun; Shigeru Kashihara; Doudou Fall; Assadarat Khurat

doi:10.1109/ICSEC.2018.8712755

Distinguishing drone types based on acoustic wave by IoT device

Nappaphol Siriphun
, Shigeru Kashihara
, Doudou Fall
, Assadarat Khurat

Computer Science Academic Group

Mahidol University
Nara Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Citations (Scopus)

Abstract

A drone detection system is essential to protect people from misuses, infringement on privacy, and other harms by drones. In this paper, we investigate to distinguish drone types based on acoustic wave. Our proposed drone detection technique employs Dejavu that is an acoustic fingerprint identification tool and random forest to classify types of drones. In the study, we first collected a dataset for three types of drones: DJI Spark, Quadcopter AR Drone 2.0, and Parrot Mambo drone. Then, we compared the detection performance based on the dataset. The experimental results showed that each drone sound can be classified by random forest.

Original language	English
Title of host publication	2018 22nd International Computer Science and Engineering Conference, ICSEC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538681640
DOIs	https://doi.org/10.1109/ICSEC.2018.8712755
Publication status	Published - 2 Jul 2018
Event	22nd International Computer Science and Engineering Conference, ICSEC 2018 - Chiang Mai, Thailand Duration: 21 Nov 2018 → 24 Nov 2018

Publication series

Name	2018 22nd International Computer Science and Engineering Conference, ICSEC 2018

Conference

Conference	22nd International Computer Science and Engineering Conference, ICSEC 2018
Country/Territory	Thailand
City	Chiang Mai
Period	21/11/18 → 24/11/18

Keywords

Acoustic fingerprint
Acoustic wave
Drone
IoT
Random forest
Sound
UAV

Access to Document

10.1109/ICSEC.2018.8712755

Cite this

Siriphun, N., Kashihara, S., Fall, D., & Khurat, A. (2018). Distinguishing drone types based on acoustic wave by IoT device. In 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018 Article 8712755 (2018 22nd International Computer Science and Engineering Conference, ICSEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSEC.2018.8712755

@inproceedings{59ec89342a0549a7b519a20893cd6f25,

title = "Distinguishing drone types based on acoustic wave by IoT device",

abstract = "A drone detection system is essential to protect people from misuses, infringement on privacy, and other harms by drones. In this paper, we investigate to distinguish drone types based on acoustic wave. Our proposed drone detection technique employs Dejavu that is an acoustic fingerprint identification tool and random forest to classify types of drones. In the study, we first collected a dataset for three types of drones: DJI Spark, Quadcopter AR Drone 2.0, and Parrot Mambo drone. Then, we compared the detection performance based on the dataset. The experimental results showed that each drone sound can be classified by random forest.",

keywords = "Acoustic fingerprint, Acoustic wave, Drone, IoT, Random forest, Sound, UAV",

author = "Nappaphol Siriphun and Shigeru Kashihara and Doudou Fall and Assadarat Khurat",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 22nd International Computer Science and Engineering Conference, ICSEC 2018 ; Conference date: 21-11-2018 Through 24-11-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ICSEC.2018.8712755",

language = "English",

series = "2018 22nd International Computer Science and Engineering Conference, ICSEC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 22nd International Computer Science and Engineering Conference, ICSEC 2018",

}

Siriphun, N, Kashihara, S, Fall, D & Khurat, A 2018, Distinguishing drone types based on acoustic wave by IoT device. in 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018., 8712755, 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018, Institute of Electrical and Electronics Engineers Inc., 22nd International Computer Science and Engineering Conference, ICSEC 2018, Chiang Mai, Thailand, 21/11/18. https://doi.org/10.1109/ICSEC.2018.8712755

Distinguishing drone types based on acoustic wave by IoT device. / Siriphun, Nappaphol; Kashihara, Shigeru; Fall, Doudou et al.
2018 22nd International Computer Science and Engineering Conference, ICSEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8712755 (2018 22nd International Computer Science and Engineering Conference, ICSEC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distinguishing drone types based on acoustic wave by IoT device

AU - Siriphun, Nappaphol

AU - Kashihara, Shigeru

AU - Fall, Doudou

AU - Khurat, Assadarat

PY - 2018/7/2

Y1 - 2018/7/2

N2 - A drone detection system is essential to protect people from misuses, infringement on privacy, and other harms by drones. In this paper, we investigate to distinguish drone types based on acoustic wave. Our proposed drone detection technique employs Dejavu that is an acoustic fingerprint identification tool and random forest to classify types of drones. In the study, we first collected a dataset for three types of drones: DJI Spark, Quadcopter AR Drone 2.0, and Parrot Mambo drone. Then, we compared the detection performance based on the dataset. The experimental results showed that each drone sound can be classified by random forest.

AB - A drone detection system is essential to protect people from misuses, infringement on privacy, and other harms by drones. In this paper, we investigate to distinguish drone types based on acoustic wave. Our proposed drone detection technique employs Dejavu that is an acoustic fingerprint identification tool and random forest to classify types of drones. In the study, we first collected a dataset for three types of drones: DJI Spark, Quadcopter AR Drone 2.0, and Parrot Mambo drone. Then, we compared the detection performance based on the dataset. The experimental results showed that each drone sound can be classified by random forest.

KW - Acoustic fingerprint

KW - Acoustic wave

KW - Drone

KW - IoT

KW - Random forest

KW - Sound

KW - UAV

UR - https://www.scopus.com/pages/publications/85066471447

U2 - 10.1109/ICSEC.2018.8712755

DO - 10.1109/ICSEC.2018.8712755

M3 - Conference contribution

AN - SCOPUS:85066471447

T3 - 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018

BT - 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Computer Science and Engineering Conference, ICSEC 2018

Y2 - 21 November 2018 through 24 November 2018

ER -

Siriphun N, Kashihara S, Fall D, Khurat A. Distinguishing drone types based on acoustic wave by IoT device. In 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8712755. (2018 22nd International Computer Science and Engineering Conference, ICSEC 2018). doi: 10.1109/ICSEC.2018.8712755

Distinguishing drone types based on acoustic wave by IoT device

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this