Machine Learning for Identifying an Endengered Brazilian Psittacidae Species

Abstract

Bird population census is an important indicator in conservation programs. However, the process of detecting and identifying particular species is time-consuming and challenging, often being conducted in remote locations. In this scenario, the development of automated acoustic systems for bird monitoring is crucial. In this study, we propose a simple but effective 3-step approach for identifying the Amazona rhodocorytha, an endangered Brazilian parrot, among 4 other species belonging to the same family. This approach consists of a pre-processing step, a feature extraction step using the MFCC algorithm and a classification step by employing a Artificial Neural Network. Results show that the proposed approach is both suitable and robust for this type of application, achieving excellent classification results of up to 98% accuracy.

Keywords: machine learning, neural networks, bird detection, MFCC, bird identification