Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques.
(Publisher abstract provided.)
Downloads
Similar Publications
- Witnessing community violence and its consequences: Changes across middle school
- An enhanced computational method for age-at-death estimation based on the pubic symphysis using 3D laser scans and thin plate splines
- Bone Mineral Density Adult Age Estimation in Forensic Anthropology: A Test of the DXAGE Application