A new fractal H-tree pattern based gun model identification method using gunshot audios

Abstract Background Gun model identification (GMI) is a complex issue for digital forensics examiners/professions. Because the GMI process is a highly costed process, and it is generally detected manually. A sound classification model is presented in this research to decrease the cost of the GMI and automate this process. Material and method The primary objective of this research is to present a new intelligent audio forensics tool. Therefore, a new gunshot dataset was collected, and the collected dataset includes 2130 audios of the 28 gun models. This dataset can be downloaded using http://web.firat.edu.tr/sdogan/Gun_S_Dogan.rar link. The presented fractal H-tree pattern-based classification method is applied to these audios to obtain results. This method has three fundamental phases, and these are feature extraction, the most informative features selection, and classification. This method uses both a fractal textural generator and statistical features. By deploying tunable q-factor wavelet transform (TQWT), a multileveled feature generation method is created to generate both low-level and high-level features. The recommended fractal H-tree pattern and statistical feature extraction functions generate features at each level. Neighborhood component analysis (NCA) chooses the most informative features. In the classification phase, the support vector machine (SVM) and k nearest neighbor (kNN) classifiers are used. Results The recommended fractal H-tree pattern-based method yielded 96.10% and 90.40% by employing kNN and SVM, respectively. Conclusion The calculated results and findings denoted the high classification capability of the presented fractal H-tree pattern-based method for gun model classification using gunshot audios. Also, this research shows that a new audio forensic tool can be developed by employing the presented method for GMI.