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Machine Learning for Identifying an Endengered Brazilian Psittacidae Species
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
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