Impulsive Acoustic Event Localization and Classification involve identifying and determining the location of sudden, transient acoustic events (such as explosions or impacts) and categorizing them based on their characteristics. This is often done using arrays of microphones or sensors strategically placed in an area to triangulate the source of the impulsive sound. Classification algorithms may then analyze the waveform and frequency content to determine the type of event.
We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.
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