Dear colleagues, On behalf of my coauthors, I am pleased to share our recent publiction in Marine Mammal Science:
Cuartas‐Marulanda, D., Leon‐Lopez, B., Isaza, C., & Romero‐Vivas, E. (2026). SOSnet A Real-Time Low-Cost AI Powered Alarm System for Cetacean Distress Call Detection. Marine Mammal Science, 42(4), e70259 . Abstract Cetaceans are prone to facing deadly situations near coastal areas, from potential vessel collisions to natural stranding events. In particular situations, animals produce distress calls that may signal a pending negative situation. In such situations, stranding response networks and authorities could benefit from an alert system using real-time passive acoustic monitoring, which requires adequate call identification and to set a reliable alarm to trigger a response. Therefore, an alarm system for S10 gray whale putative distress calls is presented. The system, trained with a set of 1177 two-second segments with S10 calls, uses a Resnet-18 convolutional neural network and transfer learning to identify the S10 call with a precision of 0.9891. Once the S10 call is confirmed by redundancy within a time frame, an alarm is sent through Short Message Service to members of the local stranding response network. The low-cost system is easy to replicate and upgrade by adding more models, and BirdNET front end facilitates its widespread use. Best regards, Braulio Leon-Lopez, Ph.D. Postdoctoral Researcher, SECIHTI Fellowship Acoustics and Signal Processing Research Group / Natural History Museum Centro de Investigaciones Biológicas del Noroeste (CIBNOR) -- -------------------------------------------------------------------------- *Este correo puede contener datos personales, por lo que su distribución es responsabilidad de la persona servidora pública que lo emite.*
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