# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 5 Conclusions  
In this paper, we present a self-created UAV sound database designed for UAV localization, focusing on distance, height, and azimuth and predicting the drone's side relative to the microphone array. To validate the utility of the dataset, we implemented a deep neural network (DNN) model and demonstrated that the dataset is effective for these tasks. Such a system has practical potential for ground-based UAV monitoring in real-world applications, including airspace surveillance, infrastructure protection, and acoustic detection in areas where visual or RF-based systems are limited. The results indicate that our model, trained using this dataset, achieves strong performance, with an average MAE of 0.5 m for distance and height. The azimuth error, on average, is around 1 degree, demonstrating the model's ability to accurately estimate the direction of the sound source. Furthermore, the model reliably predicts the position of the side of the drone, with an average error below 10 degrees, showcasing the ability to classify the drone's orientation relative to the microphone array. On the other hand, RMSE for distance, height, and azimuth does not vary significantly, indicating stable and consistent predictions for these parameters. However, the RMSE for the side angle is notably higher, suggesting greater variability in these predictions. This implies that while the model can classify the drone's general orientation well, fine-grained distinctions in its exact side positioning may be more challenging to capture. Our analysis also reveals that the Mel Spectrogram outperforms other feature extraction methods, such as STFT, LFCC, MFCC, and the Bark Spectrogram, to provide the most accurate results. This suggests that Mel Spectrograms are particularly well-suited for UAV localization tasks involving acoustic data. Additionally, we show that specific parameters, such as the audio frequency sample rate and key feature extraction parameters--like FFT size, hop length, and the number of coefficients--significantly impact the model's performance. Our experiments show that using very low parameter values in feature extraction leads to a noticeable drop in model performance, likely due to insufficient resolution in the time or frequency domain. Increasing these values improves accuracy, but only up to a certain point. Beyond that, further gains are minimal while computational demands rise sharply. This indicates that selecting moderate parameter settings can offer an effective balance between accuracy and efficiency. These findings underscore the importance of carefully considering both the dataset and the feature extraction process when designing UAV sound source localization systems. The results suggest that with proper training and parameter tuning, deep learning models can achieve high levels of accuracy in real-world UAV localization tasks. In conclusion, our study not only introduces a valuable resource in the form of the UAV sound database but also provides insights into effective methodologies for UAV localization using acoustic signals. All the data and code related to this research have been made publicly available under a GitLab repository, ensuring transparency and reproducibility. Although the current version of UaVirBASE includes data from only one UAV, it presents a fully described and reproducible setup for UAV localization using spatial audio. The dataset is accompanied by detailed metadata, the documentation of the hardware configuration, and a publicly available training code. This will enable others to replicate the setup and develop their own datasets for related research applications. We believe this research contributes to the advancement of UAV-based localization systems and lays a foundation for future work to improve sound-based localization in complex UAV environments. **Author Contributions:** [PERSON] contributed to the introduction, future conditions, description of results, and deep neural network. [PERSON] contributed to hardware and conclusions.