Acoustic detection of Unmanned Aerial Vehicles in flight with respect to the signal-to-noise ratio using machine learning

Nowadays, unmanned Aerial vehicles (UAV) are more and more commonly found in the world for a variety of activities. It has become an important task to detect and classify them in order to manage their potential malicious intentions. Current detection and classification methods mostly rely on feature extraction and the use of artificial neural networks that can be heavily impacted by the signal-to-noise ratio (SNR). The approach proposed aims to solve a two-class problem (presence or absence of an UAV) by extracting information on the content of the acoustic source, and then presenting it as input to a neural network. The features used are strongly inspired by the harmonic sound emitted by UAV. Attention is also given on the SNR of the scene. Indeed, various disturbing sources such as trains, helicopters or footsteps are involved from an open library. These signals are added to our recordings of UAVs in flight by tuning their amplitude to obtain different SNRs. The aim is to establish the performance of the detection system as a function of the SNR of the scene.