Leveraging time-based acoustic patterns for ecosystem analysis

• Published in: Neural computing & applications Vol. 36; no. 32; pp. 20513 - 20526
• Main Authors: Castro-Ospina, Andrés E., Rodríguez-Marín, Paula, López, José D., Martínez-Vargas, Juan D.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2024; Springer Nature B.V
• Subjects: Acoustics; Artificial Intelligence; Artificial neural networks; Audio data; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Feature recognition; Image Processing and Computer Vision; Machine learning; Neural networks; Original Article; Pattern recognition; Probability and Statistics in Computer Science; Sound; Soundscapes; Vegetation; Ecological acoustic indices; 1D convolutional neural networks; Passive acoustic monitoring; Soundscape; Ecoacoustics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10157-7

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.