Whistles characterisation using artificial intelligence: responses of short-beaked common dolphins (Delphinus delphis) to a bio-inspired acoustic mitigation device

Understanding cetacean whistles is crucial for assessing their social interactions, behaviors, and responses to anthropicactivities. However, to detect and dissociate different kinds of whistles within acoustic records remains challenging. Wedeveloped an innovative semi-automatic deep learning approach (DYOC) to rapidly extract whistle contours from audiorecordings, using YOLOv8m for detection and ResNet18 for identification. Applied to 808 minutes of audio recordings of wildfree-ranging short-beaked common dolphin from the Bay of Biscay, France, DYOC enabled the annotation of 8,730 contours6 times faster than manual annotation. Their features (such as duration, frequency range, and number of inflections) werethen compared based on dolphin behavior, presence of fishing nets, and the DOLPHINFREE acoustic beacon’s influence.Beacon activation led to significant frequency shifts and lower Signal-to-Noise Ratios, while during activation and deactivationphases, whistles were longer with more inflections. A dimension reduction technique (UMAP) revealed gradients betweenarchetypal whistle shapes. This study provides the first characterisation of whistle features for a population of short-beakedcommon dolphins in the Bay of Biscay. The proposed methodological approach has the potential to be applied to the study ofwhistles across a wide range of research areas, species and applications related to animal behaviour.