## 3 Experiments  
We constructed an environment and collected the dataset for identifying audio signals (male/female/child/no object) using the stereo microphone mounted on a drone. The directional stereo microphone was utilized, and the data acquisition environment is as illustrated in Figure 2. Ideally, attaching the microphone directly to the drone would have been more representative of a real-world scenario; however, due to physical constraints, we opted to place the microphone on a tripod, as shown in the figure, with a drone hovering above them. The drone hovered at an altitude ranging between approximately 2.5 m and 3.0 m, while the tripod was set up at a height of 1.7 m to position the microphone. The loudspeaker was installed at distances of 5 m, 10 m, and 20 m from the microphone, and considering that the speech signals might originate from a position lower than the microphone, the loudspeaker was placed on the ground. Here, we utilized a SONY PCM-A10 voice recorder as a microphone and a GENELEC 8010A as a loudspeaker. The model proposed in this paper also predicts the horizontal angle; hence, it was possible to position the loudspeaker from 0 degrees to 180 degrees at 20-degree intervals. Figure 2(a) displays the configuration of the loudspeaker, while Figure 2(b) shows an actual photograph of the recording site. Since rotating the microphone was much more feasible than changing the physical location of the loudspeaker, the recordings were captured by adjusting the distance of the loudspeaker and rotating the microphone to cover the required angles. The recordings of speech and drone noise were carried out separately, and the collected audio samples were mixed. Although 25 different models of drones were utilized, only about 20 models were actually used in the creation of the dataset. The voice actors for men, women, and children each comprised about 30 individuals, with children being defined as those under the age of 13. This is because students attend elementary school until the age of 13 in Korea. In addition, middle and high school students were not included as their voices contain elements of both children and adults. Each speaker had a set of 20 sentences related to distress situations (e.g., \"Help mel\"). These recorded voices were appropriately edited and used, with recordings of individual voices created without overlap and any overlapping instances were mixed randomly. Uncontrollable noises captured during outdoor recordings, such as passing vehicles, were excluded from the mix. Only recordings captured in as quiet an environment as possible were utilized, and after mixing various voices, drone noises were also mixed in. Ultimately, a total of 50,000 audio samples each lasting 30 s were created, with 500 samples set aside as a test dataset. Figure 3: Recording configuration depending on speaker placement. (**a**) The loudspeaker is positioned at intervals of 20 degrees, ranging from 0 to 180 degrees. (**b**) An actual photograph of the recording site is displayed. Figure 2: The data acquisition configuration in an outdoor environment. Due to limitations in the physical setup, we chose to mount the microphone on a tripod with a drone flying overhead. The drone maintained a height varying between roughly 2.5 m and 3.0 m, whereas the tripod was adjusted to a height of 1.7 m to properly place the microphone. The loudspeaker was positioned at varying distances from the microphone, specifically at 5, 10, and 20 m, respectively. \[WMA(d_{j})=0.05\cdot d_{j-2}+0.1\cdot d_{j-1}+0.7\cdot d_{j}+0.1\cdot d_{j+1}+0.05 \cdot d_{j+2},\quad j\in[0,9] \tag{3}\]  
Based on the compensation described in Equation (3), the performance regarding the direction of origin is measured as follows:  
\[D_{\text{direction}}=\sum_{j=0}^{9}(WMA(\hat{d_{j}})-WMA(d_{j}))^{2} \tag{4}\]  
Here, note that our method involves estimating a total of ten azimuth angles at 20-degree intervals, ranging from 0 to 180 degrees. Consequently, we have ten distinct azimuth angles, labeled from 0 to 9.