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.
Highlights
Rice–duck farming, a traditional organic farming method, uses hybrid ducks released in a paddy field for weed and pest control [1]
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
This study was undertaken to detect mallards based on acoustic information
Summary
Rice–duck farming, a traditional organic farming method, uses hybrid ducks released in a paddy field for weed and pest control [1]. One difficulty posed by mallard farming is that a sord of mallards must be gathered to a specific area in a paddy field They trample down rice, which produces stepping ponds in which it is difficult to grow rice. Another shortcoming is that weed control effects are not obtained in areas outside the range of mallard activities For this case, accurate position estimation of mallards in a paddy field is necessary. 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. 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.
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