| To: | "" <> |
|---|---|
| Subject: | New publication: SOSnet A Real-Time Low-Cost AI Powered Alarm System for Cetacean Distress Call Detection |
| From: | "Braulio Leon Lopez " <> |
| Date: | Mon, 17 Aug 2026 17:15:18 +0000 |
|
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
. https://doi.org/10.1111/mms.70259
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)
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