RT journal article
T1 Iterative deep learning for cetacean whistle detection in the Strait of Gibraltar
A1 Marquez Rodríguez, Alba
A1 Pérez Gimeno, Neus
A1 Benítez Aragón, Daniel
A1 Muñoz Arroyo, Gonzalo
A1 Cruz Muñoz, Andrés de la
A2 Biología
K1 Convolutional neural networks
K1 Deep learning
K1 Ecoacoustics
K1 Passive acoustic monitoring
K1 Strait of Gibraltar
K1 Underwater acoustics
AB Deep learning has shown remarkable potential for complex signal recognition, yet its deployment in noisy real-world environments remains a major challenge. This study presents an adaptive deep learning framework for acoustic signal detection, demonstrated through the identification of cetacean whistles in the Strait of Gibraltar, a mandatory migratory bottleneck where several species coexist with intense maritime traffic and complex acoustic conditions. Passive acoustic monitoring (PAM) enables long-term assessment of marine mammal presence and behavior, yet its application remains challenging due to overlapping anthropogenic and environmental sounds and the large volumes of data generated. The proposed framework combines transfer learning, semi-supervised iterative fine-tuning, and confidence-threshold calibration to automate the analysis of PAM data and improve robustness in acoustically variable conditions. Pre-trained bioacoustic models (BirdNET and Perch) were repurposed as feature extractors and coupled with custom classifiers to assess generalization across domains. Model performance was evaluated using standard deep learning metrics, including accuracy, recall, and F1-score. Models were validated using a clean benchmark dataset and a real-world deployment dataset characterized by substantial anthropogenic and geophonic noise. While baseline models achieved over 0.95 accuracy on clean data, their performance degraded under realistic noise conditions (whistle F1-score  0.10). The best-performing model, fine-tuned with local data and semi-supervised iterative validation, achieved an F1-score of 0.88 and improved recall across deployments. Confidence-threshold optimization further enhanced adaptability, supporting both automated and expert-assisted monitoring workflows. The final model was tested across three independent acoustic deployments spanning different seasons and locations. Results demonstrated robust whistle detection with minimal annotation effort. This work provides a reproducible, AI-driven framework for signal detection in noisy environments, demonstrating how adaptive learning and threshold calibration can extend the applicability of deep neural networks to real-world acoustic monitoring and contribute to the development of automated, ecologically meaningful soundscape analysis systems.
PB Elsevier
SN 0952-1976
YR 2026
FD 2026-03
LK http://hdl.handle.net/10498/38514
UL http://hdl.handle.net/10498/38514
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026