Author:
Padovese Bruno Tavares,Padovese Linilson Rodrigues
Abstract
AbstractAvian 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.
Publisher
Cold Spring Harbor Laboratory
Reference42 articles.
1. Audacity® software is copyright © 1999-2018 Audacity Team. Web site: https://audacityteam.org/. It is free software distributed under the terms of the GNU General Public License. The name Audacity® is a registered trademark of Dominic Mazzoni.
2. Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring
3. Phylogenetic and ecological determinants of the neotropical dawn chorus
4. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
5. Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献