Bioacoustic Dataset of African and Florida Manatee Vocalizations for Machine Learning Applications, 2020-2022

This data package presents a comprehensive acoustic library of manatee vocalizations for machine learning (ML) and classifier development. It includes recordings from two species, African and Florida manatees, sampled across four locations. The species are combined due to the acoustic similarity of their vocalizations, providing a diverse and representative training set for ML algorithms. The dataset consists of 0.5-second WAV clips categorized as either containing manatee vocalizations (MV, n=18,129 clips) or not (Noise, n=23,444 clips). MV clips may include multiple vocalizations or truncated calls. All clips were manually verified by two researchers with expertise in manatee acoustics. Recordings were collected using stationary hydrophones deployed in natural habitats, with variable signal-to-noise ratios (SNR) resulting from changes in distance between the vocalizing manatees and the recorders. Background noise across sites is relatively low, with minimal anthropogenic noise; caution is advised when applying models to noisier environments. No dolphin species are believed to be present at the recording sites, and models trained on this dataset should be used cautiously in dolphin-inhabited regions to avoid false positives. If you use this dataset, please reach out to the listed contacts, we are interested in learning how it supports your work.