Using Wing Flap Sounds to Distinguish Individual Birds

Abstract

To monitor male and female bird nest attendance, the traditional methods are physical markings for identification. This paper presents two methods-Principal Component Analysis (PCA) combined with K Nearest Neighbor (KNN) and Cross-Correlation classification-that can identify individual birds based on the sounds of their wing flaps without the need for physically marking the birds. The study conducted on three male Zebra Finch birds resulted in identification accuracy ranging from 70% to 100%. To distinguish between individual birds, the conventional invasive technique involves capturing, marking, releasing, and recapturing. However, this approach has various limitations and drawbacks. As an alternative solution, researchers have resorted to using bird vocalizations for identification purposes. This research shows that birds can also be uniquely identified from the sounds produced by their wing flaps.