In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment; next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.

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.