Adapting deep learning models to new acoustic environments - A case study on the North Atlantic right whale upcall

Padovese, Bruno; Kirsebom, O. S.; Frazão, Fábio; Evers, Clair; Beslin, Wilfried; Theriault, James A.; Matwin, Stan

doi:10.1016/j.ecoinf.2023.102169

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Cited by 6 publications

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Leveraging time-based acoustic patterns for ecosystem analysis

Castro-Ospina,

Rodríguez-Marín,

López

et al. 2024

Neural Comput & Applic

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Passive acoustic monitoring (PAM) is an effective, non-intrusive method for studying ecosystems, but obtaining meaningful ecological information from its large number of audio files is challenging. In this study, we take advantage of the expected animal behavior at different times of the day (e.g., higher acoustic animal activity at dawn) and develop a novel approach to use these time-based patterns. We organize PAM data into 24-hour temporal blocks formed with sound features from a pretrained VGGish network. These features feed a 1D convolutional neural network with a class activation mapping technique that gives interpretability to its outcomes. As a result, these diel-cycle blocks offer more accurate and robust hour-by-hour information than using traditional ecological acoustic indices as features, effectively recognizing key ecosystem patterns.

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