Using wing flap sounds to distinguish individual birds

Abstract

Ornithologist often need to recognize individual birds, but traditional invasive methods, such as capturing, marking, releasing, and recapturing of birds, have limitations. To overcome this, researchers use non-invasive alternatives, such as bird vocalizations. In our study, we used wing flap sounds of three male Zebra Finch birds for individual recognition. We achieved identification accuracies ranging from 55% to 100% by using a combination of Principal Component Analysis-K-Nearest Neighbor (PCA-KNN) and Cross-Correlation method on training data and testing data. PCA-KNN allows for dimensionality reduction and pattern recognition, while the Cross-Correlation method bases data analysis on shifting data elements. Our approach can be applied to other bird species and is becoming more accessible due to technological advancements. Non-invasive methods for bird identification are becoming increasingly popular, and our study demonstrates the potential for using wing flap sounds to recognize individual birds.