DOI: 10.5281/zenodo.10948974
title: Dataset for: Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem.

description: This is the supporting data for:

Clink, D.J., Kim, J., Cross‐Jaya, H., Ahmad, A.H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T.T., Thi, H.N. and Chi, T.N., 2025. Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the “torch for R” ecosystem. Ecology and Evolution, 15(7), p.e71678.

Summary of acoustic and image data.

Data are in the "data_only" folder. Please see publication for details: https://doi.org/10.1002/ece3.71678.








Dataset



Dataset ID



Country

 



Species



Number of Gibbon samples



Number of Noise samples



Sample rate





Danum Valley Conservation Area Training Data



Train-Val



Malaysia



Grey gibbon



502



2,254



16 kHz





Jahoo Training Data



Train-Val



Cambodia



Crested gibbon



213



2,130



32 kHz





Maliau Basin Test Data



Test-Generalizable



Malaysia



Grey gibbon



147



81



48 kHz





Dakrong Nature Reserve Test Data



Test-Generalizable



Vietnam



Buff cheeked gibbon



45



173



16 kHz





Danum Valley Test Data



Test-Final



Malaysia



Grey gibbon



383



2,905



16 kHz





Jahoo Test Data



Test-Final



Cambodia



Crested gibbon



296



5,684



32 kHz






Summary of data and scripts.

To run the scripts download the "data" and "results" folders from Zenodo and add to your project directory.

Link to GitHub Repository: https://github.com/DenaJGibbon/torch-for-R-gibbons

   


R Script
Summary of Referenced Folders
Top-Level Data or Results Folders Referenced


Part 1a. Variability benchmarking results
Training/test image folders (Danum, Jahoo, Combined); model run results for variability benchmarking.
data/training_images_sorted/, results/part1/


Part 1b. Evaluate variability benchmarking results
Evaluation output folders.
results/part1/


Part 2a. Train CNNs over multiple epochs
Training/test data for all sites; performance outputs for initial CNN evaluations (Cambodia, Malaysia).
data/training_images_sorted/, results/part2/


Part 2b. Train CNNs (cont.)
Model output folders (binary & multi-class); rerun AUC updates; model type labels.
results/part2/


Part 3a. Data augmentation
Augmented training images (Danum, Jahoo, Combined); test sets; model run outputs based on augmented data.
data/DataAugmentation/, data/training_images_sorted/, results/part3/


Part 3b. Evaluation data augmentation
Outputs from evaluating models trained on augmented data; model labels by type.
results/part3/


Part 3c. Eval. augmentation on different test set
Evaluation outputs for external test data (Maliau + Vietnam); multi-class model run outputs.
TestData/, results/part3/


Part 4b. Comparison with BirdNET
BirdNET outputs for Jahoo, Danum, and Combined data; test output from jittered data augmentation.
results/part4/, results/part3/


Part 5. Final model performance
Final evaluation images (per site); top-performing model folders; final performance CSVs.
data/CombinedImagesWAEvaluation/, results/part5/


Part 6. Deploy Model over PAM data
Longer sound files (Jahoo); final trained model checkpoint (.pt file) used for inference.
data/WideArrayEvaluation/, results/part3/


Part 7. Call Density Plots
Files for call density visualizations: GPS, selections, manually verified wavs or images (TP/FP).
data/calldensityplots/