RT Journal Article
SR Electronic
T1 A Machine Learning Approach to the Recognition of Brazilian Atlantic Forest Parrot Species
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2019.12.24.888180
DO 10.1101/2019.12.24.888180
A1 Bruno Tavares Padovese
A1 Linilson Rodrigues Padovese
YR 2019
UL http://biorxiv.org/content/early/2019/12/27/2019.12.24.888180.abstract
AB Avian survey is a time-consuming and challenging task, often being conducted in remote and sometimes inhospitable locations. In this context, the development of automated acoustic landscape monitoring systems for bird survey is essential. We conducted a comparative study between two machine learning methods for the detection and identification of 2 endangered Brazilian bird species from the Psittacidae species, the Amazona brasiliensis and the Amazona vinacea. Specifically, we focus on the identification of these 2 species in an acoustic landscape where similar vocalizations from other Psittacidae species are present. A 3-step approach is presented, composed of signal segmentation and filtering, feature extraction, and classification. In the feature extraction step, the Mel-Frequency Cepstrum Coefficients features were extract and fed to the Random Forest Algorithm and the Multilayer Perceptron for training and classifying acoustic samples. The experiments showed promising results, particularly for the Random Forest algorithm, achieving accuracy of up to 99%. Using a combination of signal segmentation and filtering before the feature extraction steps greatly increased experimental results. Additionally, the results show that the proposed approach is robust and flexible to be adopted in passive acoustic monitoring systems.