# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 4 Future Directions  
Based on our current work and an analysis of future requirements, we identified several key areas for further development and improvement. The current dataset exhibits limitations in three key spatial dimensions:  
* Azimuth Resolution--the localization is presently constrained to a 45\({}^{\circ}\) angular increment; thus, we aim to increase angular resolution.
* Altitude Range--the existing recordings cover a limited vertical spectrum (10 and 20 m). We plan to extend this with low- and high-altitude scenarios.
* Radial Distance--the current operational radius from the acoustic head is limited to 20 m. Future trials will focus on extending this range to include greater distances.  
Expanding spatial coverage in all three dimensions--along the azimuth, altitude, and radial distance--is expected to improve the precision and robustness of UAV localization. However, increasing the resolution and range also means handling more data and more complex scenarios, which significantly raises processing demands. This can impact real-time performance and require greater computational efficiency. As a result, improvements such as reducing input data size and simplifying model structures will be necessary to maintain accuracy while keeping latency and resource usage within practical limits.  
Another identified limitation is the absence of multiple UAV types and overlapping sources in the dataset. At present, our recordings are limited to a single UAV model and single-object scenarios. To address this, we plan to expand our fleet and conduct additional recording sessions using different UAV models individually to increase acoustic diversity. In future stages, we also intend to perform sessions with multiple UAVs operating simultaneously, enabling research into more complex tasks such as sound source separation, interference handling, and multi-target classification.  
In parallel with the spatial expansion, we are also working on complementary datasets focused on UAV detection and classification tasks, which will support broader use cases beyond localization. Looking ahead, we plan to develop a dataset involving dynamic UAV movements tracked via GPS to enable future research into real-time acoustic tracking and trajectory estimation. These additions will extend the practical relevance of our work to more complex scenarios involving moving targets and diverse drone behaviors.  
Additionally, we are exploring the development of a similar project and dataset tailored for an ad hoc network using a mesh of independent sensors. Such a system poses unique challenges, requiring the design and implementation of new software and hardware solutions distinct from those presented in this study.  
As we continue refining our device and dataset, it is evident that creating a large, high quality and comprehensive dataset requires substantial time and resources. The sheer diversity of commercially available UAVs, not to mention custom-built or one-of-a-kind vehicles, makes it impractical for a single team to catalog them all. We are considering publishing our software and hardware as an open-source project to address this challenge. This approach would invite contributions from other interested parties, enabling them to create their own datasets.