SeaAlert framework enhances analysis of noisy maritime distress calls

Researchers have developed SeaAlert, a new framework designed to improve the analysis of maritime distress communications. This system utilizes transformer-based severity classification and LLM-based information extraction to handle noisy and non-standard distress messages. To overcome the lack of labeled data, a synthetic data generation pipeline was created, producing varied distress messages that were then degraded with simulated noise and processed by an ASR system. The evaluation demonstrated that SeaAlert's transformer models are more resilient to noise and variations in communication than traditional methods, while its LLM-based extraction proved more effective than regex approaches. AI

IMPACT This research could lead to more reliable and faster responses to maritime emergencies by improving the accuracy of AI systems processing distress calls.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology for analyzing maritime distress communications. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Global Maritime Distress Safety System
• Gotit.pub
• Hugging Face
• ScienceCast
• SeaAlert
• Yehudit Aperstein

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