One-Class Bioacoustic Detector for Monitoring the Critically Endangered Pied Tamarin ( Saguinus bicolor )
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The pied tamarin ( Saguinus bicolor ) is a critically endangered primate with a small geographic range that includes fragmented urban forest mosaics in Amazonia, where habitat subdivision and anthropogenic actions complicate its survival and monitoring. Passive acoustic monitoring (PAM) offers a convenient, noninvasive way to track this species, yet open-set rainforest soundscapes make single-species detection challenging. We present a machine-learning pipeline with a very low false-positive rate, appropriate for downstream inference. The method combines a band-pass filter (5–10 kHz), Perch bioacoustic embeddings (deep learning), and a One-Class SVM (OCSVM) applied to sliding windows of continuous audio recordings to detect S. bicolor calls. We train on a reduced dataset of labeled calls and validate against diverse out-of-class audio (birds, anurans, anthropophony, and geophony/insects), then test on long, cross-site recordings. The approach achieves high discrimination on held-out negatives and produces very low false-positive rates in realistic, continuous audio. Finally, we pair detections with a single-site occupancy model in a cross-site setting to illustrate end-to-end utility for conservation monitoring and to estimate the false-negative detection probability in recordings from pied tamarin populations in a different geographic region. Our strategy provides a tool for PAM of S. bicolor that requires minimal manual labeling effort and can be adapted to other open-set, single-species monitoring scenarios. We also release a convenient Python package (sauim-detector), installable via pip, that processes an audio file and produces detection timestamps as an Audacity label file (.txt), enabling faster manual verification.
Highlights
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Open-set bioacoustic detector for the pied tamarin . We introduce an open-set pipeline—band-pass (5–10 kHz) → Perch embeddings → one-OCSVM—tailored to S. bicolor , filling a gap with no prior species-specific detector.
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Bird-trained Perch embeddings transfer to primates . We show that Perch embeddings trained on birds generalize to S. bicolor vocalizations, enabling cross-taxon reuse without retraining.
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Band-pass filtering reduces background false positives and improves separability . On our evaluation sets, the 5–10 kHz filter removed all background FPs and shifted ROC curves up/left, increasing AUC.
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Robust cross-site detection on long recordings with very few false positives . In a ∼10 min Mindú Park test, the detector prioritized precision, with an observed false-positive rate of 0.03, and improved AUC from 0.74 (raw) to 0.83 (filtered).
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End-to-end monitoring via occupancy modeling . We couple detections with a single-site occupancy estimator and derive a closed-form MLE to estimate the cross-site false-detection probability.
