arXiv:2606.19568v1 Announce Type: cross Abstract: Acoustic gunshot detection is a problem with applications across civilian public safety, military operations, and wildlife conservation, yet the field lacks a rigorous exploration of feature extraction techniques with a focus on generalization to realistic data. The mixed effectiveness of commercial gunshot detection and classification systems indicates an open problem that is not adequately addressed by the current literature. In this paper, we present a systematic investigation of common feature extraction techniques using a dataset of 23,000
The increasing sophistication of AI and acoustic analysis techniques, combined with growing demand for advanced security and monitoring solutions, drives this focused research.
Improved gunshot detection accuracy has significant implications for public safety, military operations, and environmental protection, potentially reducing response times and enhancing situational awareness.
This systematic investigation aims to provide a more robust understanding of feature extraction for acoustic gunshot classification, moving beyond current mixed effectiveness and potentially leading to more reliable systems.
- · Defence industry
- · Public safety agencies
- · Security technology providers
- · Wildlife conservation organizations
- · Criminal organizations (potentially)
- · Ineffective legacy acoustic monitoring systems
More accurate and reliable acoustic gunshot detection systems will become available.
Faster and more precise responses to gunfire incidents could lead to reduced casualties and improved law enforcement effectiveness.
Widespread deployment of such systems could alter urban planning, security protocols, and even the public's sense of safety.
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Read at arXiv cs.AI