# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 2 Materials and Methods
### Hardware  
For this study, we used a single UAV model--the DJI Mavic 3 Cine--which was sufficient to demonstrate and validate the recording system's functionality. This drone was selected due to its commercial availability, stable flight behavior, and representative acoustic characteristics. The inclusion of additional UAV models is a concept planned for future work and can be integrated into the current system without major changes. We opted to utilize microphones with a supercardioid pattern, as they offer superior noise rejection compared to omnidirectional microphones. While this choice may restrict the application of certain methods, such as TDoA, as the object's sound may be more challenging to isolate when the microphone is facing away from it, it does provide additional information regarding direction. The microphone directed at the sound source will capture it more precisely. Given these considerations, we selected the Rade NTG2 shotgun microphone [40]. This microphone has a relatively high dynamic range of 113 dB and a frequency range of 20 Hz to 20 kHz. Although it does not encompass infrasound or ultrasound, it should be adequate to capture the diverse range of noises generated by the rotors of the UAV. The next crucial component is the audio interface. It should be user-friendly and have sufficient connectivity to accommodate eight microphones. Additionally, it should have a high sampling rate and bit resolution. We chose to use the Behringer UMC1820 [41]. This interface surpasses the selected microphones for the available audio frequency band. Furthermore, it offers a 96 kHz sampling rate, which should provide sufficient audio quality for our purposes. For static localization, we opted to refrain from utilizing Global Positioning System (GPS) technology. However, further research is underway to develop dynamic localization capabilities with UAVs in motion, utilizing high-resolution GPS data. Instead, we chose to employ conventional methods of distance measurement. For this purpose, we utilized a measuring wheel and verified the results using a laser rangefinder. This enabled us to determine the horizontal distance while the remaining components of the position were measured using sensors onboard the UAV. Due to the multifaceted nature of sound wave propagation, influenced by factors such as temperature, humidity, and atmospheric pressure, we incorporated data from a local weather station. The strategic placement of the station at the recording location provided access to weather data and enabled the collection of wind conditions, including direction and speed. This additional information significantly enhanced our dataset and potentially facilitated the more effective de-noising of the recordings. However, we chose not to perform de-noising ourselves but instead provided the recordings in their raw, unaltered form. To facilitate the aforementioned data streaming for app development, we selected the SenseCap S212 8-in-1 Weather Sensor [42], which boasts a compact design while offering a comprehensive suite of sensors, easy mounting, and before-mentioned remote data streaming capabilities. For data recording, we also decided to record the whole recording session \"from the side\". We used Zoom h4 essential [43] with two connected Behringer B-1 [44] microphones. These are the primary \"off-the-shelf\" hardware components utilized in constructing a database. Now, let us proceed with describing how they were assembled for recording. We designated our recording rig as \"Acoustic Head\"; henceforth, we will refer to the constructed device by this name. In this setup, we recorded the DJI Mavic 3 Cine UAV (Figure 3). As previously mentioned, we utilized eight microphones divided into two levels (four microphones per level). Each level comprised four arms, with a microphone mounted on each arm (Figure 4). These levels are independent, and the distance between them can be adjusted as per the requirement (Figure 5).