# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 1 Introduction  
Humans possess an innate ability to localize sounds in their environment, seamlessly determining the direction and distance of a sound source. This ability is crucial for interpreting and responding to dynamic scenes, as it allows for the integration of auditory and visual information. Inspired by this human capability, sound source localization (SSL) has emerged as a fundamental area of research in acoustic signal processing [1]. It has a broad range of applications, from robotics [2], urban noise monitoring [3], and visual scene analysis [4] to wildlife monitoring [5] and IoT systems [6]. In recent years, the growing prevalence of unmanned aerial vehicles has further amplified interest in SSL, particularly for detecting and localizing unmanned aerial vehicles (UAVs) in scenarios such as security operations, surveillance, airspace monitoring, and even military applications [7]. However, UAV localization introduces unique challenges, including the blending of rotor noise with environmental sounds, varying flight altitudes, rapid and dynamic movements, and strong ego-noise from motors and propellers, whichresult in low signal-to-noise ratios (SNRs) and dynamic acoustic transmission paths [8]. To address these complexities, UAV localization systems often rely on various technologies, including RF radio, radar, acoustic, vision-based methods, or fusion sensors [9]. Each of these approaches has unique advantages and limitations [10]. RF radio methods are widely used due to their cost-effectiveness and suitability for long-range detection, but they can be affected by signal interference or deliberate suppression by UAVs [11]. Radar systems, on the other hand, are robust and provide wide spatial coverage and high angular resolution [12]. Yet, they face challenges distinguishing UAVs from other low-altitude, slow-moving objects such as birds. Vision-based methods provide high precision and excellent tracking capabilities; however, they struggle in conditions of low visibility or environments with visual obstructions [13]. Acoustic localization using microphone arrays complements these technologies by leveraging the unique rotor noise generated by UAVs, which is difficult to conceal and remaining unaffected by low visibility or radio interference. However, acoustic methods face challenges in environments with high background noise levels, where distinguishing UAV sounds from other ambient sounds can be difficult. Additionally, the range of acoustic detection is typically shorter compared to RF or radar-based systems, limiting its application in large-scale monitoring scenarios [14]. Fusion sensor systems, which integrate data from multiple modalities, such as combining two [15; 16; 17] or three sources [18], have demonstrated their effectiveness in overcoming the limitations of individual methods. By leveraging the complementary strengths of RF, radar, vision-based, and acoustic techniques, these systems significantly enhance the reliability and accuracy of UAV detection and localization, especially in complex and dynamic environments [9]. However, despite their effectiveness, fusion systems can be resource-intensive, requiring substantial hardware, integration, and computational infrastructure investment. This makes them less feasible for cost-sensitive applications. In contrast, acoustic localization stands out as a more cost-effective alternative. With relatively inexpensive hardware, such as microphone arrays, and the ability to utilize computationally efficient classical algorithms, acoustic systems provide a practical solution for budget-conscious UAV monitoring scenarios. Despite their limited detection range and susceptibility to noise compared to RF or radar-based methods, their affordability and simplicity make acoustic localization an appealing choice for applications with constrained budgets. To address the challenges of sound source localization, numerous methods have been developed, broadly categorized into classical approaches and artificial intelligence (AI)-based methods [19].