Advancing Dolphin Acoustic Monitoring: A Comprehensive Whistle Classification Framework

Dolphins are widely recognized as intelligent marine mammals with sophisticated communication and echolocation. Accurately classifying their whistles is essential for understanding their communication patterns and monitoring their population size, structure, and distribution. In this study, we assembled a large, high-quality dataset of Indo-Pacific bottlenose dolphin (Tursiops aduncus) whistle signals collected at the Chimelong Ocean Kingdom. The dataset included multiple whistle categories, including a whistle type that has not previously been available for research. We then applied convolutional neural networks (CNNs) for classifying whistle signals, using five CNN architectures to analyze the signals. Model performance was evaluated using mean average precision (mAP), and the best-performing model achieved 0.929 in mAP on the test set, demonstrating that CNN-based approaches can effectively distinguish among different whistle classes. To probe robustness, we also introduced noise at defined SNR levels to increase testing complexity and assess the stability of the classifier. BELLHOP acoustic propagation modeling was used to generate channel impulse responses. These simulated signals were combined with the original signal data to construct an augmented training set. The results indicate that this augmentation enhanced the robustness of the classification model. Differentiating the whistle types is crucial as whistle categories may reflect variation in communication structure, behavioral context, or group-level acoustic patterns. Therefore, the proposed approach can support large-scale bioacoustic analysis and provide useful information for future studies on dolphin communication, behavior, and conservation.