论文标题
枪支声音：枪支音频样本的数字取证符合人工智能
Sound of Guns: Digital Forensics of Gun Audio Samples meets Artificial Intelligence
论文作者
论文摘要
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要的应用。现有的大多数作品都依赖于空间多样的麦克风传感器的临时部署来捕获同一枪声的多个复制品，从而可以准确检测和识别声源。但是，在诸如犯罪现场取证之类的情况下，很难获得经过精心控制的设置，从而使上述技术不适用且不切实际。我们介绍了一种新型技术，该技术需要关于记录设置的零知识，并且完全不可知麦克风和射击器的相对位置。我们的解决方案可以识别枪支的类别，口径和模型，在由3655个样本组成的数据集中达到超过90％的精度，这些样本从YouTube视频中提取。我们的结果表明，应用卷积神经网络（CNN）在枪伤分类中的有效性和效率消除了对临时设置的需求，同时显着提高了分类性能。
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.
