Feature set comparison for automatic bird species identification

Feature set comparison for automatic bird species identification

Lopes, Marcelo Teider and Silla, Carlos Nascimento and Koerich, Alessandro Lameiras and Kaestner, Celso Antonio Alves

Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics 2011

Abstract : This paper deals with the automated bird species identification problem, in which it is necessary to identify the species of a bird from its audio recorded song. This is a clever way to monitor biodiversity in ecosystems, since it is an indirect non-invasive way of evaluation. Different features sets which summarize in different aspects the audio properties of the audio signal are evaluated in this paper together with machine learning algorithms, such as probabilistic, instance-based, decision trees, neural networks and support vector machines. Experiments are conducted in a dataset of recorded songs of three bird species. The experimental results compare the performance of the features sets and different classifiers showing that it is possible to obtain very promising results in the automated bird species identification problem. © 2011 IEEE.