###### Abstract  
This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice-duck farming.  
dealy-and-sum beamforming; microphone array; position estimation; rice-duck farming; remote farming +
Footnote †: journal:  
To mitigate or resolve these difficulties, we evaluated an efficient management system created by locating and identifying mallards and their natural enemies using acoustic information that can be sensed widely in a paddy field. Furthermore, one can provide effective control to elucidate exactly when, where, and what kinds of natural enemies are approaching. This study was conducted to develop a position estimation system for mallards in a paddy field. Developing stable production technology is strongly demanded because rice produced by mallard farming trades at a high price on the market. For this system, we expect technological development and its transfer to remote farming [2], which is our conceptual model for actualizing smart farming.  
This paper presents a direction and position estimation method for a sound source of pre-recorded mallard calls using acoustic information with arrayed microphones combined with delay-and-sum (DAS) beamforming. Using acoustic information, the approach can detect mallards that are occluded by stalks or grass. Based on the results, we inferred that an efficient management system can be actualized from locating and identifying mallards and their natural enemies using acoustic information that can widely sense a paddy field. We developed a prototype system with a 64-microphone array installed on our originally designed and assembled mount. To reproduce the sounds obtained using a microphone in advance, we conducted a simulated experiment in an actual environment to evaluate the estimation accuracy of the method.  
This paper is structured as follows. Section 2 briefly reviews state-of-the-art acoustic and multimodal methods of automatic bird detection from vision and audio modalities. Subsequently, Sections 3 and 4 present the proposed localization method using an originally developed microphone system based on DAS beamforming. The experimental results obtained using our original acoustic benchmark datasets obtained using two drones are presented in Section 5. Finally, Section 6 presents the conclusions and highlights future work.