@article{ref1,
title="Preliminary investigation of acoustic identification of firearms mechanisms and its application to case investigations",
journal="Association of Firearm and Toolmark Examiners journal",
year="2021",
author="Giverts, P. and Eckert, J. and Sofer, S. and Solewicz, Y.",
volume="53",
number="4",
pages="152-158",
abstract="Firearm mechanisms make different acoustic signals during their operation. These signals can be recorded by video cameras and microphones at crime scenes. The availability of this data opens up the possibility to associate a particular firearm operation to an acoustic signal and can be used in crime investigations and at trials. For such associations, different methods can be used. The article discusses the possibility of the application of machine learning methods for the characterization and comparison of acoustic signals made by firearm mechanisms. As a preliminary investigation, two models (XGBoost and Neural Network) were trained using a specially prepared database. These models were used for the processing and analysis of acoustic signals in a video recorded by a surveillance camera at a real crime scene of an attempted murder. The results and the limitations are discussed. Further research on the subject is suggested.   Keywords: acoustic identification, firearm's mechanism sounds, machine learning, Mel-frequency cepstral coefficients, MFCC, neural network, Short-time Fourier transformation, STFT, XGBoost<p /> <p>Language: en</p>",
language="en",
issn="1048-9959",
doi="",
url="http://dx.doi.org/"
}