SEABAD is a publicly released, balanced dataset of 50,000 curated 16 kHz audio clips spanning 1,677 tropical bird species, with a dual-branch curation pipeline and MobileNetV3-Small baseline reaching 99.57% accuracy.
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Prediction-domain adaptive cross-validation is proposed as a flexible alternative to fixed random or spatial methods for reliably estimating accuracy in environmental maps.
citing papers explorer
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SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring
SEABAD is a publicly released, balanced dataset of 50,000 curated 16 kHz audio clips spanning 1,677 tropical bird species, with a dual-branch curation pipeline and MobileNetV3-Small baseline reaching 99.57% accuracy.
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Moving beyond spatial and random cross-validation in environmental modelling: a call for prediction-domain adaptive evaluation
Prediction-domain adaptive cross-validation is proposed as a flexible alternative to fixed random or spatial methods for reliably estimating accuracy in environmental maps.