Comparing neural networks against click train detectors to reveal temporal trends in passive acoustic sperm whale detections - Garrobe Fonollosa, Laia; Webber, Thomas; Brotons, José Maria; Cerdà, Margalida; Gillespie, Douglas Michael; Pirotta, Enrico; Rendell, Luke Edward (The Journal of the Acoustical Society of America, 2025)
Funding: The Direcció General de Pesca i Medi Marí del Govern de les Illes Balears - support to obtain data about Sperm Whale presence in Emile Baudot and Mallorca Channel. Additionally, we are grateful to the Fundación Biodiversidad of the Ministerio de Transición Ecológica y el Reto Demográfico for their support of projects CALMA and CALMADOS, which were conducted to assess the temporal presence of cetaceans in the sea mountains Ausiàs March and Monte Olivas. Passive acoustic monitoring (PAM) is an increasingly popular tool to study vocalising species. The amount of data generated by PAM studies calls for robust automatic classifiers. Deep learning (DL) techniques have been proven effective in identifying acoustic signals in challenging datasets, but due to their black-box nature their underlying biases are hard to quantify. This study compares human analyst annotations, a multi-hypothesis tracking (MHT) click train classifier and a DL-based acoustic classifier to classify acoustic recordings based on the presence or absence of sperm whale (Physeter macrocephalus) click trains and study the temporal and spatial distributions of the Mediterranean sperm whale subpopulation around the Balearic Islands. The MHT and DL classifiers showed agreements with human labels of 85.7% and 85.0%, respectively, on data from sites they were trained on, but both saw a drop in performance when deployed on a new site. Agreement rates between classifiers surpassed those between human experts. Modeled seasonal and diel variations in sperm whale detections for both classifiers showed compatible results, revealing an increase in occurrence and diurnal activity during the summer and autumn months. This study highlights the strengths and limitations of two automatic classification algorithms to extract biologically useful information from large acoustic datasets. Peer reviewed