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The study aims to benchmark various automated detection approaches for monitoring gibbons’ acoustic signals.

Benchmarking Automated Detection and Classification Approaches for Long‐Term Acoustic Monitoring of Endangered Species: A Case Study on Gibbons From Cambodia

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The study aims to benchmark various automated detection approaches for monitoring gibbons’ acoustic signals.
Compared performance of support vector machines (SVMs), quasi-DenseNet architecture, and transfer learning models.
Utilized BirdNET embeddings for detection with fewer training samples.
Analyzed over 130,000 hours of soundscape data for gibbon calls.
BirdNET-based models had superior performance with limited training data.
Over 12,000 verified true positive detections were achieved.
Gibbon calls predominantly occurred early in the morning, with variable detections during monsoon seasons.

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Benchmarking reveals superior detection of gibbon calls using transfer learning models in Cambodia, suggesting improved conservation methods.

Clink et al. (2026) studied this question.

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Benchmarking reveals superior detection of gibbon calls using transfer learning models in Cambodia, suggesting improved conservation methods.