Mapping Deep Learning Approaches for Acoustic Bird Species Classification in the Brazilian Pantanal - Gomes, Julia Milioranza; Daron, Isis Milena; Ventura, Thiago Meirelles (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2026: Anais do XVII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos NaturaisDO - 10.5753/wcama.2026.21136, 2026)
Automated acoustic monitoring of bird species has strong potential for biodiversity conservation, yet key questions remain regarding effective visual representations, deep learning architectures, and preprocessing strategies, particularly in biodiversity-rich regions such as the Brazilian Pantanal. This study analyzes approaches for acoustic bird species classification. Results indicate that Mel-spectrograms and CNNs are the predominant standards, while temporal segmentation is widely adopted as a preprocessing step. Additionally, less than 6% of the reviewed studies focus on Brazil, with none addressing the Pantanal, highlighting the need to combine globally pre-trained architectures with regional datasets for automated biodiversity monitoring.