## 2 Related Studies
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Year** & **Authors** & **Method** & **Dataset** \\ \hline
2011 & [PERSON] et al. [12] & Boosted HOG-LBP + SVM & original \\
2011 & [PERSON] et al. [13] & Image energy & original \\
2011 & [PERSON] et al. [14] & HOG + SVM + PNAD & original \\
2012 & [PERSON] et al. [15] & HOG + CS-LBP + linear SVM & original \\
2013 & [PERSON] et al. [16] & HOG + linear SVM & [52] \\
2014 & [PERSON] et al. [17] & linear SVM classifier & original \\
2015 & Yoshihashi et al. [18] & BS + CNN & [18] \\
2016 & [PERSON] et al. [19] & SBGS + SIFT + BoW + SVM & original \\
2016 & [PERSON] et al. [20] & CNN (ResNet) + FCNs + DeepLab & original \\
2016 & [PERSON] et al. [21] & CNN + FCN + SP & original \\
2017 & [PERSON] et al. [22] & CNN (ResNet) & [53] \\
2017 & [PERSON] et al. [23] & Faster RCNN & original \\
2018 & [PERSON] et al. [24] & Skeleton-based MPSC & [50;52] \\
2019 & [PERSON] et al. [25] & CNN & original \\
2019 & [PERSON] et al. [26] & Faster RCNN & original \\
2019 & [PERSON] et al. [27] & Faster RCNN & original \\
2019 & [PERSON] et al. [28] & CNN & original \\
2019 & [PERSON] et al. [29] & CNN (Inception-v3) & original \\
2020 & [PERSON] et al. [30] & RCNN & original \\
2020 & [PERSON] et al. [31] & CNN + RPN + Fast-RCNN & original \\
2021 & [PERSON] et al. [32] & Faster RCNN (ResNet-50) & original \\
2021 & [PERSON] et al. [33] & RNN + CNN & original \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative studies of vision-based bird detection methods reported during the last decade. Recently, vision-based detection methods have targeted not only birds, but also drones [92]. In 2019, the Drone-vs-Bird Detection Challenge (DBDC) was held at the 16 th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) [93]. The goal of this challenge was the detection of drones that appear at several image coordinates in videos without detecting birds that are present in the same frames. The developed algorithms must alert detected drones only, without alerting birds. Moreover, it is necessary to provide estimates of drone locations on the image coordinates. As a representative challenge for acoustic bird detection, bird acoustic detection [65] was conducted at the Detection and Classification of Acoustic Scenes and Events (DCASE) in 2018. Among the 33 submissions evaluated for this challenge, the team of [PERSON] et al. [44] was awarded the champion title. For this detection task, the organizer provided 10 h of sound clips, including negative samples with human limitations of bird sounds. The algorithms produced a binary output of whether or not the sounds were birds, based on randomly extracted sound clips of 10 s. There are differences between our study and the extraction challenge of the coordinates of birds inferred from sound clips of the same period. To improve the accuracy for wide applications, the algorithms developed for the challenge incorporated domain adaptation, transfer learning [94], and generative adversarial networks (GANs) [95]. By contrast, our study is specialized for application to mallard farming in remote farming [2]. In comparison to a large number of previous studies mentioned above, the novelty and contributions of this study are as follows:  
* The proposed method can detect the position of a sound source of pre-recorded mallard calls output from a speaker using two parameters obtained from our originally developed microphone arrays;
* Compared with existing sound-based methods, our study results provide a detailed evaluation that comprises 57 positions in total through three evaluation experiments;
* To the best of our knowledge, this is the first study to demonstrate and evaluate mallard detection based on DAS beamforming in the wild. Based on the underlying method used in drone detection [96] and our originally developed single-sensor platform [97], this paper presents a novel sensing system and shows experimentally obtained results obtained from outdoor experiments.