Gunshots detection, identification, and classification: Applications to forensic science

Area of Science:

• Acoustics
• Forensic Science
• Machine Learning

Background:

• The widespread use of audio sensors in surveillance and personal devices necessitates effective gunshot detection.
• Gunshot-based event detection and forensic analysis are critical for law enforcement and crime scene reconstruction.

Purpose of the Study:

• To analyze the acoustic characteristics of gunshots and their forensic applicability.
• To review machine learning technologies for gunshot detection, identification, and classification.
• To identify trends and challenges in developing advanced security and forensic analysis systems.

Main Methods:

• Analysis of acoustic properties of gunshots and influencing variables.
• Comprehensive literature review of existing gunshot detection, identification, and classification technologies.
• Detailed examination of machine learning components: dataset construction, feature extraction, and classifier selection.

Main Results:

• Gunshot acoustics present both opportunities and limitations for forensic applications.
• Machine learning, particularly deep learning neural networks, shows significant promise for advancing gunshot detection.
• Challenges exist in comparing diverse algorithms due to data and evaluation metric variations.

Conclusions:

• Deep learning-driven neural networks are expected to dominate future gunshot detection technologies.
• This research provides a foundation for developing novel security systems and forensic analysis tools.
• Further research is needed to standardize evaluation criteria for machine learning algorithms in this field.