Implementation of Audio Recognition System for Unmanned Aerial Vehicles

Solis, Edgar R.; Shashev, Dmitriy; Shidlovskiy, Stanislav

doi:10.1109/sibcon50419.2021.9438906

Cited by 4 publications

(4 citation statements)

References 5 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%

“…The authors claimed that these limitations can be fixed by extending training audio recordings, combining audio-visual data into the detection process, or integrating many acoustic sensor outputs into array processing systems. Similar research on acoustic-based UAV recognition based on analyzing UAV rotor sounds was presented in [54]. Hexacopters and quadcopters were employed for creating drone and non-drone datasets from scratch: gradually starting from 21 samples and reaching 696 samples.…”

Section: Acoustic-based Detectionmentioning

confidence: 99%

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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: Acoustic-based Detectionmentioning

confidence: 99%

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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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“…[1] used short-time Fourier Transform (STFT) to extract the sound signatures of UAVs during flight and proposed a real-time detection system based on support vector machine. Solis et al [2] discussed the performance of support vector machine and convolutional neural network classifiers for UAV sound detection. They used Mayer cepstrum coefficients of UAV sound as the classification features, and the results showed that the detection accuracy of the convolutional neural network classifier for UAVs was only 60-69%.…”

Section: Introductionmentioning

confidence: 99%

Sound Target Detection Under Noisy Environment Using Brain-Computer Interface

Wang

Liu

Shi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.

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