A Large-Scale UAV Audio Dataset and Audio-Based UAV Classification Using CNN

Wang, Yaqin; Chu, Zhiwei; Ku, Ilmun; Smith, E. Cho; Matson, Eric T.

doi:10.1109/irc55401.2022.00039

Cited by 8 publications

(4 citation statements)

References 127 publications

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“…In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], RF sensors [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], audio sensors [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and camera sensors using visual UAV characteristics [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2 .…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…Nevertheless, the sound produced by propeller blades is frequently employed for detection because it has a comparatively larger amplitude. Numerous research works have examined the sound produced by drones, using characteristics like frequency, amplitude, modulation, and duration to identify a drone’s existence [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ].…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

“…However, the authors suggested that in the future, small and noise-free data should be verified in a more populated outside area. Another CNN-based multi-class UAV sound classification with a large-scale dataset was proposed in [ 57 ]. The large-scale UAV dataset introduced contains audio files of 10 distinct UAVs, ranging from toy drones to Class I drones, with each file including a 5-second recording of the flying drone sound.…”

Section: Drone Detection Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

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The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

Section: Drone Detection Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Seidaliyeva,

Ilipbayeva,

Taissariyeva

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In general, an autoencoder works with two main functions: the encoder part which functions to compress the input data, and the decoder part which reconstructs the data to its original form. Lastly, another option for audio classification issues is CNN [20][21][22][23]. It was first developed in 1995 by Lecun and Bengio and is very commonly used in image-based classification [24].…”

Section: Introductionmentioning

confidence: 99%

Deep learning and acoustic approach for mechanical failure detection in industrial machinery

Qurthobi,

Maskeliūnas

2023

J. Phys.: Conf. Ser.

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Research into acoustic signal-based failure detection has developed into a subject that has attracted the attention of many researchers in recent years. Acoustic signal data collection can be performed without having to interrupt or stop the operation of the machine to be inspected. Therefore, it is very beneficial for the development of nondestructive testing and predictive maintenance. In this study, a collection of pump sound recordings that are part of the Malfunctioning Industrial Machine Investigation and Inspection dataset, known as the MIMII dataset, is used as test material. Several deep learning algorithms such as long short-term memory (LSTM), gate recurrent unit (GRU), autoencoder, and convolutional neural network (CNN) were involved and compared to determine their ability to detect failures. Based on the training results with 300 epochs and a learning rate of 10−6 it was found that CNN produced the classification with the highest accuracy compared to the other algorithms. In addition, the CNN algorithm is also capable of performing classification amidst the problem of imbalance in the amount of data.

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MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats

Yuan,

Yang,

Nguyen

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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A Large-Scale UAV Audio Dataset and Audio-Based UAV Classification Using CNN

Cited by 8 publications

References 127 publications

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review

Deep learning and acoustic approach for mechanical failure detection in industrial machinery

MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats

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