## 4 Discussion and Conclusions  
We discussed the applicability of robot audition techniques to understand the soundscape dynamics of bird vocalizations in forests. We focused on the elevation information of localized bird songs in addition to azimuth information. A speaker test for DOA esti \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Time Slot (s)** & **\# of Localized Songs** & **Azimuth Variation (rad\({}^{2}\))** & **Elevation Variation (rad\({}^{2}\))** \\ \hline A (0–100) & 37 & 0.87 & 0.07 \\ B (550–650) & 31 & 1.15 & 0.10 \\ C (650–750) & 46 & 0.81 & 0.04 \\ D (750–850) & 57 & 0.76 & 0.06 \\ E (900–1000) & 45 & 0.48 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The variations in azimuth and elevation of localized sounds in each time slot. Figure 7: A song distribution on the polar coordinate of azimuth (angle) and elevation (radius) in each time slot (A–E) in Figure 5.  
mation of replayed vocalizations showed that the observed DOA of a distant target sound matched well with expected values in both azimuth and elevation angle when no other bird vocalizations were present near the target. A field observation of several individuals reflected well the ecologically plausible structures of the soundscape of bird species in the experimental forest, showing vertical species structures of bird vocalizations. Several statistical indices of localized songs can also summarize the detailed changes in the structures of the soundscape. The localization of bird vocalization is based on various components including both hardware (i.e., microphone arrays) and software (i.e., HARK). Finer resolution of the estimated DOA would be an important factor for this purpose because many sound sources other than those of the target species or individuals always coexist in fields. Improving the resolution of the MUSIC spectrum by increasing the number of steering vectors (i.e., candidate directions for DOA estimation) would be useful, but requires a great deal of computational cost, especially for azimuth-elevation estimation. The interpolation of the MUSIC spectrum used for finer 2D localization of bird vocalizations with two microphone arrays [22] would be efficient in this case. The balanced settings of DOA resolution along with interpolation would be beneficial for long-term analyses for biodiversity surveys. Further consideration of the effects of microphone channel geometry on the localization accuracy of bird vocalizations is part of our future work. A systematic comparison of other sound source localization and separation techniques, including adaptive filtering, is important for more practical applications of robot audition techniques to bird behavioral observations. In this study, we employed the simplest and most standard methods (SEVD-MUSIC and GHDSS) employed in HARK, expecting that it will provide a baseline result because the method has been shown to be applicable to field observations of birds in previous studies as introduced in Introduction. We also expected that using such a simple method would be appropriate to examine the basic effects of acoustic noise in the natural environment, and advanced methods can improve the results (e.g., the MUSIC based on generalized singular value decomposition (GSVD-MUSIC) for better speech recognition [23], and the MUSIC method based on incremental generalized eigenvalue decomposition (iGEVD-MUSIC) for drone audition [24]). Also, this research has an experimental rather than a theoretical aspect. Still, we believe it is important for considering the trends and challenges in robotic applications to show an example of the application of robotics to field observations of natural sounds. At the same time, we believe that a report on sound source localization in both elevation and azimuth angles is particularly important for birds that can fly. The report will contribute to the practical application of related techniques to ecoacoustics, as microphone arrays are expected to be used more frequently in this field.