A 15-Category Audio Dataset for Drones and an Audio-Based UAV Classification Using Machine Learning

Abstract

The popularity of Unmanned Aerial Vehicles (UAVs), aka drones, has increased rapidly in recent years. UAVs are becoming easily accessible to more users. Malicious intentions can erode public safety when least expected. Current methods used for UAV detection systems include computer vision, radar, radio frequency and audio approaches. We choose the audio method for its high accuracy, low computational requirement and low cost. However, the lack of publicly available datasets is one of the main bottlenecks for developing an audio-based UAV detection and classification system. To fill this gap, we select 15 different UAVs, ranging from toy hand drones to Class I drones and record a total of 8120 s length of audio data generated from the flying UAVs. To the best of our knowledge, the proposed dataset is the largest audio dataset for UAVs so far. We further implement a Convolutional Neural Network (CNN) model for 15-class UAV classification and trained the model with the collected data. The average test accuracy of the trained model is 98.7% and the test loss is 0.076.