It is realy great that this publication is open access.
Dig into underwater acoustics and up to date research on marine mammals.
https://link.springer.com/book/10.1007/978-3-031-77022-7
#BioAcoustics #ecoacoustics #acoustics #nature #science #marinemammals
We just updated our preprint "A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana" to its last version 🐦🎶, which has been just accepted to Ecological Informatics
📍 Doñana National Park, Spain
📊 Passive Acoustic Monitoring
🤖 YOLOv8 + BirdNET
📄 Read it here 👉 https://arxiv.org/abs/2503.15576
Passive Acoustic Monitoring is a key tool for biodiversity conservation, but the large volumes of unsupervised audio it generates present major challenges for extracting meaningful information. Deep Learning offers promising solutions. BirdNET, a widely used bird identification model, has shown success in many study systems but is limited at local scale due to biases in its training data, which focus on specific locations and target sounds rather than entire soundscapes. A key challenge in bird species identification is that many recordings either lack target species or contain overlapping vocalizations, complicating automatic identification. To address these problems, we developed a multi-stage pipeline for automatic bird vocalization identification in Doñana National Park (SW Spain), a wetland of high conservation concern. We deployed AudioMoth recorders in three main habitats across nine locations and manually annotated 461 minutes of audio, resulting in 3749 labeled segments spanning 34 classes. We first applied a Bird Song Detector to isolate bird vocalizations using spectrogram-based image processing. Then, species were classified using custom models trained at the local scale. Applying the Bird Song Detector before classification improved species identification, as all models performed better when analyzing only the segments where birds were detected. Specifically, the combination of detector and fine-tuned BirdNET outperformed the baseline without detection. This approach demonstrates the effectiveness of integrating a Bird Song Detector with local classification models. These findings highlight the need to adapt general-purpose tools to specific ecological challenges. Automatically detecting bird species helps track the health of this threatened ecosystem, given birds sensitivity to environmental change, and supports conservation planning to reduce biodiversity loss.
Also, I just came back from the SIBECOL & AEET meeting in Pontevedra
I had the opportunity to present some advances from SEANIMALMOVE, showing how we’re applying ecoacoustics to both avian monitoring in terrestrial environments and marine mammal monitoring underwater
I kept some of the latest results for the upcoming manuscripts 😉 but the presentation was strong enough to win Best Predoc Oral Communication
#Ecoacoustics #Ecology #ScienceCommunication #SIBECOL #AEET #AIforConservation #BirdNET
🎉 After almost a year since the first submission, our manuscript has been accepted!
This is my first time as first author — it's been a challenging journey, but incredibly rewarding. I've learned so much that I’ll apply to future manuscripts and research projects
I hope the paper will be available very soon — stay tuned! 📄✨
(You have the previous version as preprint: https://arxiv.org/abs/2503.15576)
#Research #Ecoacoustics #PAM #Ecology #AIforEcology #Manuscript
New research shows snapdragons can “hear” bees and boost their nectar. The plant world just got louder. #PlantIntelligence #PollinationScience #EcoAcoustics
Clemens Pitschke
Alba Márquez-Rodríguez
Geekoo
Wetland Project