# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 1 Introduction
Classical techniques, such as Time Difference of Arrival (TDoA), Direction of Arrival (DoA), and beamforming, rely on well-established signal processing frameworks [20]. These methods are computationally efficient and perform well in controlled conditions but often face limitations in dynamic, noisy, or complex environments. On the other hand, AI finds applications in many fields, ranging from digital watermarking [21], 3D printing material optimization [22], and process planning [23] to deepfake generation [24] or victim verification [25], showcasing its versatility across diverse domains. In acoustics, AI-based methods leverage the power of machine learning and deep learning models to capture intricate spatial and acoustic relationships. These approaches offer enhanced robustness and adaptability, particularly in environments with significant background noise or non-stationary sound sources. By learning directly from the data, AI-driven methods address many of the challenges associated with classical approaches, although this is at the cost of requiring large, high-quality datasets [26] and considerable computational resources. Recognizing the growing prominence of artificial intelligence and its potential to transform acoustic localization, we undertook a significant initiative: creating a dedicated sound dataset for UAV localization. This dataset was meticulously designed to address the multifaceted challenges of UAV SSL. It includes UAV rotor noise recorded at varying altitudes, distances, and angles relative to the microphone array--front, back, right, and left of the drone--and background noise from the urban environment. The primary goal of this work was to develop a structured, extensible, and fully replicable experimental setup for UAV localization--including the dataset, hardware configuration, and training process--that reflects the complexities of real-world spatial audio recording. While this first version includes a single UAV, it establishes a replicable framework and serves as a foundation for broader multi-UAV datasets. In doing so, it enables the training and evaluation of AI algorithms designed for UAV localization, fostering the development of more robust and adaptive systems. The dataset also enables the classification of the UAV's orientation relative to the microphone array, determining whether the drone is positioned at the front, back, right, or left. These detailed positional data allow for the more precise determination of UAV locations, supporting advancements in localization techniques. Although several UAV acoustic datasets have been published [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38], they primarily focus on detection or classification tasks and often lack spatial detail or comprehensive labeling. Many of these datasets contain mono or stereo recordings with limited metadata and are not designed for full sound source localization. In contrast, UaVirBASE offers high-resolution 8-channel audio captured at 96 kHz and a 32-bit depth, with detailed annotations covering azimuth, distance, height, and the drone's relative side. This enables spatial reasoning and directional modeling far beyond the capabilities of existing datasets. Our initiative bridges a critical gap in available resources for the research community, emphasizing the importance of high-quality data in driving innovation and addressing the challenges of modern UAV monitoring requirements. As shown in Figure 1, the structure of this article includes the following sections: Section 1 is the Introduction. Section 2, titled Materials and Methods, presents the hardware and software components used for data collection, along with the data annotation process. Section 3 details the experimental setup, including the structure of the UaVirBASE dataset, the selected audio features, the architecture and training process of the deep neural network model, the evaluation metrics used, and the results obtained. Section 4 discusses future directions, and Section 5 concludes the study. Figure 1: Structure and organization of the article.