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Acoustic-Based Drone Detection Using Neural Networks – A Comprehensive Analysis
Summary
This study developed and evaluated a drone detection system for urban environments using acoustic features processed by neural networks, testing multiple neural network architectures for classifying drone presence from audio and acoustic signals. The system demonstrated effective drone detection using sound-based methods as an alternative or complement to radar-based approaches.
The article presents and describes the implementation of research on the detection of a drone in an urban environment using of the sound features. The methods of drone detection were recognized on the basis of modeling and evaluation of the features of the audio and acoustic signal. The authors proposed the use of a neural network model for the needs of drone detection taking into account acoustic measurements in an anechoic chamber and in an urban environment. The final part presents the obtained results of the drone detection. For the purposes of detection, a neural network model was used in order to recognize the obtained images of the spectograms of sound sources.
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