Beyond the Blind Spot: A Transparency-First Approach to AI Sound-Vision Radar

Le résumé fourni par la source

Multimodal situational awareness dashboards that combine sound and vision are increasingly deployed in security, facility, and safety monitoring, yet relatively little attention has been given to how these systems communicate the status of their underlying sensor data. In some prototype and demonstration systems, simulated values may be presented in ways that resemble live sensor outputs, making it difficult for operators to distinguish between actual observations and generated information. This article presents AI Sound and Vision Radar, a dashboard that integrates real-time visual object detection and tracking with real-time acoustic capture, spectral analysis, and sound source bearing estimation, together with a tool-calling large language model that enables natural language interaction. The audio and visual streams are associated through a lightweight bearing-gated fusion layer to provide a unified situational view. The system is designed around a transparency-first architectural pattern in which every sensing and reasoning module first attempts genuine hardware or API access, reports its connectivity status explicitly, and requires the interface to verify that status before presenting any value as live rather than simulated. We describe the system architecture and report the functional verification conducted during development, including interface rendering, multi-frame tracking persistence, occlusion handling, bearing-gated fusion behaviour, and end-to-end validation of the language model tool-calling workflow. We suggest that explicit sensor-provenance disclosure is a useful design consideration for multimodal monitoring systems and complements Explainable AI approaches that primarily focus on interpreting model decisions rather than communicating the origin and availability of sensor data.

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