枪声：枪声样本数字取证与人工智能（CS LG）---用户7095611

根据炮口冲击波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有着重要的应用。 现有的大多数工程依赖于特别部署的空间多样性麦克风传感器，以捕捉同一枪击的多个复制品，从而能够准确探测和识别声源。 然而，在诸如犯罪现场取证之类的情况下，很难获得精心控制的设置，这使得上述技术不适用且不切实际。 我们介绍了一种新颖的技术，需要零知识的录音设置，是完全不知道的相对位置麦克风和射手。 我们的解决方案可以识别枪支的种类，口径和型号，在从 YouTube 视频中提取的3655个样本数据集上，准确率达到90% 以上。 我们的研究结果证明了应用卷积神经网络(CNN)进行枪击分类的有效性和效率，不再需要特定的设置，同时显著提高了分类性能。

原文题目：Sound of Guns: Digital Forensics of Gun Audio Samples meets Artificial Intelligence

原文：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.

原文作者：Simone Raponi

原文地址：https://arxiv.org/abs/2004.07948