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
Ângelo M. C. R. Borzino, Robert C. Maher, José A. Apolinário, and Marcello L. R. de Campos, "Employing wavelet-based texture features in ammunition classification," Proc. SPIE 10184, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XVI, 101840D (Presented at SPIE Defense + Security: April 10, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262282.
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