## 2 Methods
### Bird Song Localization Using HARKBird  
We used HARKBird 2.0 [13], a collection of Python scripts for bird song localization, to estimate the DOA of sound sources in recordings, using sound source localization and separation functions in HARK.  
The employed sound source localization algorithm is based on the multiple signal classification (MUSIC) [19] using multiple spectrograms obtained by short-time Fourier transformation (STFT). The MUSIC method is a widely used high-resolution algorithm, and is based on the eigenvalue decomposition of the correlation matrix of multiple signals from a microphone array. We adopted the standard eigenvalue decomposition (SEVD) MUSIC method implemented as one of the sound source localization methods in HARK. All localized sounds are separated the sounds as wave files (16 bit, 16 kHz) using geometric high-order decorrelation-based source separation (GHDSS) method [20], which is also implemented in HARK. For more details on HARKBird ([[http://www.alife.cs.i.nagoya-u.ac.jp/](http://www.alife.cs.i.nagoya-u.ac.jp/)]([http://www.alife.cs.i.nagoya-u.ac.jp/](http://www.alife.cs.i.nagoya-u.ac.jp/))\(-\)reiji/HARKBird/), accessed on 25 December 2022, see [13; 21] and on HARK, see [PERSON] et al. [12]. In order to optimize localization performance, we can adjust some parameters of HARKBird, such as the source tracking and the lower bound frequency for MUSIC, to reduce noise, etc.  
Figure 4: A panorama (**A**) and 360 degree (**B**) photos around the microphone array.  
Figure 5: A spectrogram of the whole recording and 5 time slots on which we focus in the analysis.  
We used a transfer function of the microphone array created from a numerical simulation based on the geometry of the channels of the microphone array using HARKTool5, assuming that there no effects of the body of the array unit on sound transmission. The resolution of DOA for azimuth angle was 5 degrees. The resolutions for elevation angles were 5 and 15 degrees for a speaker test and a bird observation, respectively.  
For the DOA estimation of replayed vocalizations in the speaker test, we used the limited frequency range (1800-5000 Hz) for sound source localization, which included the replayed songs. We found that the amplitude of the replayed vocalizations became weaker the farther as the microphone array was from the speaker. Therefore, we gradually decreased the threshold parameter (THRESH), which determines the minimum value of the MUSIC spectrum to detect a sound source, from 29 to 20 with increasing distance. we determined these threshold values empirically according to the acoustic condition around the microphone array. However, this resulted in HARKBird localize vocalizations of other bird species more frequently. Therefore, we manually selected the localized sounds that were detected as replayed vocalizations, and excluded other localized sounds from the analyses. We also lowered the threshold in degree to distinguish multiple sound sources in different directions when there were other sound sources in closer directions to the replayed songs. This is to avoid recognizing them as a part of the replayed vocalizations. We used default values for the other parameters of HARKBird.  
For the field observation, we focused on five 100-s time slots (A-E) during which a Blue-and-white Flycatcher (Figure 5) Sang on top of tall trees, along with other species such as Varied Tit (_Sitiparus varius_) and Coal Tit (_Periparus ater_). We focused on the behavioral changes in the individual of Blue-and-white Flycatcher, and chose the durations that well illustrated his different behavioral patterns.  
We adjusted parameters of HARKBird to localize their songs and exclude other sound sources. We plotted the distribution of songs in the polar-coordinate system representing the azimuth-elevation space for each slot. We then calculated the elevation and azimuth variations of the localized sounds to see if such statistical metrics could reflect the sound-scape structures of bird songs.