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Network Biology, 2025, 15(3): 75-89
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Artificial intelligence in acoustic ecology: Soundscape classification in the Cerrado

Bruno Daleffi da Silva, Linilson Rodrigues Padovese
Polytechnic School at the University of Sao Paulo, Brazil

Received 22 February 2025;Accepted 25 March 2025; Published online 28 March 2025;Published 1 September 2025
IAEES

Abstract
This article explores the application of machine learning techniques in acoustic ecology to classify the formations of the Brazilian Cerrado (Forest, Savanna, and Grassland) based on their soundscapes. Considering the importance of the Cerrado in biodiversity and hydrology, along with the challenges faced by the biome due to agricultural expansion, the study seeks more efficient and cost-effective methods for identifying its phytophysiognomies. Five statistical models were developed and evaluated, utilizing both traditional Machine Learning and Deep Learning, with Mel Frequency Cepstral Coefficients (MFCCs) and spectrogram images as input variables. The performance comparison of these models revealed the superiority of the Convolutional Neural Network (CNN), which, although requiring higher computational costs and training time, provided high accuracy in classifications and valuable insights through the application of the LIME explainability technique. Additionally, the study proposes a multiple classification methodology by majority voting for frequently observed events, enabling reliable classifications through models with moderate performance. The conclusion is that it is possible to classify different Cerrado formations through their acoustic landscape, and the choice of the optimal model for classification should consider a balance between accuracy, operational complexity, and efficiency. The findings of this study offer relevant guidance for future research and the application of monitoring technologies in conservation and biome recovery efforts.

Keywords Cerrado;acoustic ecology;soundscapes;Artificial Intelligence;Machine Learning;Convolutional Neural Network (CNN);Gradient Boosting;Random Forest;environmental preservation;biodiversity..


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