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This work aims to investigate the use of deep neural network to detectcommercial hobby drones in real-life environments by analyzing their sounddata. The purpose of work is to contribute to a system for detecting dronesused for malicious purposes, such as for terrorism. Specifically, we present amethod capable of detecting the presence of commercial hobby drones as a binaryclassification problem based on sound event detection. We recorded the soundproduced by a few popular commercial hobby drones, and then augmented this datawith diverse environmental sound data to remedy the scarcity of drone sounddata in diverse environments. We investigated the effectiveness ofstate-of-the-art event sound classification methods, i.e., a Gaussian MixtureModel (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network(RNN), for drone sound detection. Our empirical results, which were obtainedwith a testing dataset collected on an urban street, confirmed theeffectiveness of these models for operating in a real environment. In summary,our RNN models showed the best detection performance with an F-Score of 0.8009with 240 ms of input audio with a short processing time, indicating theirapplicability to real-time detection systems.

Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks