Citation

Giverts P, Sofer S, Solewicz Y, Varer B. Forensic Sci. Int. 2019; 306: e110099.

Affiliation

Nestlogic International Company, HaGefen 16, Ramat Gan, Israel.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.forsciint.2019.110099

PMID

31841932

Abstract

During the operation of firearms different sounds are made. The sound of a shot, the sound of a flying bullet as well as the sound of the bullet's impact have all been investigated. However, less attention has been given to the sound of the mechanical operation of firearms. This research demonstrates that different types of firearms make different acoustic signals. Moreover, signals which are made by the same firearm during different operations are different. The article discusses how these acoustic signals can be analyzed, compared, and identified in a few different ways. As a result, machine learning has been found to be the most promising for this purpose. The research proves that the presented method of analysis of acoustic signals made by firearms can be used in forensic identification. In addition, the type and stages of further investigation are defined.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Acoustic; Classification; LDA; Machine learning; Signal analysis; Sound; XGBoost