Investigation of a Neural Network for Dolphin Whistle Detection Through Heatmaps

Due to their central role in marine ecosystems, top predators such as dolphins are indispensable bioindicators in long-term ecological monitoring programs. As keystone species occupying the highest trophic levels, these predators exert top-down control by regulating prey populations and maintaining the ecosystem in balance. Their abundance, distribution and behavior are reliable indicators of ecosystem health and integrity. Several attempts have been made to quantify the behavior and abundance of dolphins by detecting their vocalizations, mostly using convolutional neural networks (CNNs) employed for pattern recognition over spectrogram images. However, the performance of automatic detection systems often strongly depends on the training distribution, making it difficult to generalize to other dolphin species, environmental conditions and recording devices. In this paper, we highlight the problem of robustness in CNN whistle detection and offer a possible direction to move forward through heatmaps: a tool to investigate which particular features in the spectrogram are guiding the CNN detection. Qualitative analysis reveals spectral and temporal features that can serve as keys to developing more robust CNN models for dolphin whistle detection.