Fine-tuned Faster-R-CNN for detection and classification of blue and fin whale calls in low-frequency audio data
Rattachement africain : us, no. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
This repository contains the weights for a fine-tuned Faster R-CNN object detection model trained to detect blue and fin whale calls in 60-second spectrograms from passive acoustic recordings. The model detects five call types: blue whale A, B, and D calls, and fin whale 20 Hz and 40 Hz calls. The model uses a ResNet-50 backbone with a feature pyramid network and was fine-tuned from a pretrained checkpoint over three rounds of human-in-the-loop refinement on high-frequency acoustic recording package (HARP) and California Cooperative Oceanic Fisheries Investigation (CalCOFI) sonobuoy recordings from the Southern California Current Ecosystem. The final training dataset comprised 11,676 annotated calls across both recording platforms. The model operates on 60-second spectrograms computed with a 1-s Hamming window and 90% overlap, band-limited to 10–150 Hz. Audio is partitioned into non-overlapping 60-second clips, spectrograms are normalized individually between 0–1, and mapped to an 8-bit integer tensor. Code for running inference or retraining the model is available at https://github.com/m1alksne/WhaleMoanDetector.
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University of California San Diego pays non établi dans la noticeUniversité ou école supérieure
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San Diego State University pays non établi dans la noticeUniversité ou école supérieure
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Scripps Institution of Oceanography pays non établi dans la noticeStructure de recherche
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Norwegian University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
University of California San Diego, San Diego State University et Scripps Institution of Oceanography, avec 1 autre affiliation.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.