Classifying a weapon based on its muzzle blast is a challenging task that has significant applications in various security and military fields. Most of the existing works rely on ad-hoc deployment of spatially diverse microphone sensors to capture multiple replicas of the same gunshot, which enables accurate detection and identification of the acoustic source. However, carefully controlled setups are difficult to obtain in scenarios such as crime scene forensics, making the aforementioned techniques inapplicable and impractical. We introduce a novel technique that requires zero knowledge about the recording setup and is completely agnostic to the relative positions of both the microphone and shooter. Our solution can identify the category, caliber, and model of the gun, reaching over 90% accuracy on a dataset composed of 3655 samples that are extracted from YouTube videos. Our results demonstrate the effectiveness and efficiency of applying Convolutional Neural Network (CNN) in gunshot classification eliminating the need for an ad-hoc setup while significantly improving the classification performance.

翻译：根据枪口爆炸对武器进行分类是一项具有挑战性的任务,在各种安全和军事领域都有重大应用,大部分现有工程依靠临时部署空间多样性的麦克风传感器,以捕捉同一枪声的多重复制品,从而能够准确探测和识别声学来源;然而,在犯罪现场法证等情况下,很难获得经过仔细控制的设置,使上述技术无法适用和不切实际;我们引入了一种新颖技术,要求对记录设置不甚了解,并且完全不理会麦克风和枪手的相对位置。我们的解决方案可以确定枪的类别、口径和型号,在由YouTube视频提取的3 655个样本组成的数据集上达到90%的准确度。我们的结果表明,在枪声分类中应用进化神经网络(CNN)消除了对自动安装的需要,同时大大改进了分类性能。