Exploiting Wavelet Scattering Transform and 1D-CNN for Unmanned Aerial Vehicle Detection

Recent advancements in Unmanned Aerial Vehicles (UAVs) have prompted concerns regarding their potential misuse. Deep-learning techniques offer superior detection capabilities compared to traditional rule-based approaches, provided the training dataset is diverse and sufficiently large. We present a UAV acoustic dataset featuring a range of UAVs, from toys to high-speed models, recorded in an open field simulating airport environments. To ensure robust multi-conditional training, the dataset is augmented with noise at specific signal-to-noise ratios (SNRs). Further, we introduce a network called WST-CNN, which has a non-trainable Wavelet Scattering Transform (WST) layer with fixed initializations as the first layer in a Convolutional Neural Network (CNN). With raw audio as an input, exempting any pre-processing, WST provides a multi-resolution time-frequency representation resilient to noise and signal deformation. As a secondary result, we introduce a 1D-F-CNN network utilizing distinctive acoustic features for UAV detection.