Leveraging time-based acoustic patterns for ecosystem analysis DOI Creative Commons
Andrés Eduardo Castro-Ospina, Paula Andrea Rodríguez Marín, José David López

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(32), P. 20513 - 20526

Published: Aug. 13, 2024

Abstract 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 challenging. In this study, we take advantage the expected animal behavior at different times day (e.g., higher activity 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 pretrained VGGish network. These feed 1D convolutional neural network class activation mapping technique that gives interpretability outcomes. As result, diel-cycle offer more accurate robust hour-by-hour than using traditional indices as features, effectively recognizing key ecosystem

Language: Английский

Characterization of soundscapes with acoustic indices and clustering reveals phenology patterns in a subtropical rainforest DOI
Yen‐Chun Lai,

Sheng-Shan Lu,

Ming‐Tang Shiao

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 171, P. 113126 - 113126

Published: Jan. 27, 2025

Language: Английский

Citations

0
LEAVES: An open-source web-based tool for the scalable annotation and visualisation of large-scale ecoacoustic datasets using cluster analysis DOI Creative Commons
Thomas R. Napier, Euijoon Ahn, Slade Allen‐Ankins

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103026 - 103026

Published: Feb. 1, 2025

Language: Английский

Citations

0
Long-term biome biomonitoring DOI
Qiang Ding, Yijie Tong, Lulu Li

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 69 - 94

Published: Jan. 1, 2025

Language: Английский

Citations

0
Leveraging time-based acoustic patterns for ecosystem analysis DOI Creative Commons
Andrés Eduardo Castro-Ospina, Paula Andrea Rodríguez Marín, José David López

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(32), P. 20513 - 20526

Published: Aug. 13, 2024

Abstract 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 challenging. In this study, we take advantage the expected animal behavior at different times day (e.g., higher activity 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 pretrained VGGish network. These feed 1D convolutional neural network class activation mapping technique that gives interpretability outcomes. As result, diel-cycle offer more accurate robust hour-by-hour than using traditional indices as features, effectively recognizing key ecosystem

Language: Английский

Citations

0