Alba Márquez-Rodríguez

🆕 New paper out in Eng. applications of AI! We propose iterative deep learning for detecting cetacean whistles in one of the noisiest marine regions: the Strait of Gibraltar 🐋🔊 While baseline models collapsed under noise, we reached 0.88 F1 🔗 sciencedirect.com/science/articl…

Not only birds! Can AI detect cetacean movements whistles in one of the noisiest seas on Earth? Yes! 🐬 In our new paper, we show it’s possible using deep learning in the Strait of Gibraltar. 🔗 shorturl.at/kNI55

All code, data pipelines, and tools are open source to support reproducible and scalable PAM workflows. Check it on GitHub!

Choosing the “optimal” ROC threshold is not always optimal for ecology. We show how confidence threshold calibration can balance automation vs expert review depending on monitoring goals.

🆕 New paper out in Eng. applications of AI! We propose iterative deep learning for detecting cetacean whistles in one of the noisiest marine regions: the Strait of Gibraltar 🐋🔊 While baseline models collapsed under noise, we reached 0.88 F1 🔗 sciencedirect.com/science/articl…

@GrunCrow sciencedirect.com/science/articl…

I'm thrilled to share with you our lattest publication with the brilliant @GrunCrow as the first author. We propose here a two-stage pipeline for analyzing audios from a acoustic monitoring programme in Doñana; 1) using a detector of vocalizations, 2) a fine-tuned classificator.

First week of June I went to the SIBECOL & AEET meeting in Pontevedra I presented advances from #SEANIMALMOVE on using ecoacoustics for bird & marine mammal monitoring Some results are saved for upcoming papers — but the talk still won Best Multidisciplinary Predoc Oral Comm!

All data and methods are shared and publicly available: 📰Paper: arxiv.org/abs/2503.15576 📁Dataset: huggingface.co/datasets/GrunC… 🤖Simplified Repository: github.com/GrunCrow/Bird-… 🤖Main Repository: github.com/GrunCrow/BIRDe…

General models like BirdNET are powerful, but local adaptation is key, but sometimes even fine-tuning or custom classifiers are not enough. By separating detection and classification, we boost precision and reduce noise in PAM workflows.

We just updated our paper “A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana”, officially accepted in Ecological Informatics 📍 Doñana National Park 📊 Passive Acoustic Monitoring 🤖 YOLOv8 + BirdNET 📰 arxiv.org/abs/2503.15576

Alba Márquez-Rodríguez (@GrunCrow) has done an amazing work developing a Bird Song Detector. Many audios are empty or present overlapping bird songs. Previously applying a detector allows to select audios with songs, lower confidence score and improving classifications.