SOSnet A Real‐Time Low‐Cost AI Powered Alarm System for Cetacean Distress Call Detection

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.