UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset

Abstract

This article presents UaVirBASE, a publicly available dataset for the sound source localization (SSL) of unmanned aerial vehicles (UAVs). The dataset contains synchronized multi-microphone recordings captured under controlled conditions, featuring variations in UAV distances, altitudes, azimuths, and orientations relative to a fixed microphone array. UAV orientations include front, back, left, and right-facing configurations. UaVirBASE addresses the growing need for standardized SSL datasets tailored for UAV applications, filling a gap left behind by existing databases that often lack such specific variations. Additionally, we describe the software and hardware employed for data acquisition and annotation alongside an analysis of the dataset’s structure. With its well-annotated and diverse data, UaVirBASE is ideally suited for applications in artificial intelligence, particularly in developing and benchmarking machine learning and deep learning models for SSL. Controlling the dataset’s variations enables the training of AI systems capable of adapting to complex UAV-based scenarios. We also demonstrate the architecture and results of the deep neural network (DNN) trained on this dataset, evaluating model performance across different features. Our results show an average Mean Absolute Error (MAE) of 0.5 m for distance and height, an average azimuth error of around 1 degree, and side errors under 10 degrees. UaVirBASE serves as a valuable resource to support reproducible research and foster innovation in UAV-based acoustic signal processing by addressing the need for a standardized and versatile UAV SSL dataset.

Keywords

• acoustics
• sound source localization
• audio dataset
• microphone arrays
• unmanned aerial vehicle