Recently, deep learning-based methods have drawn huge attention due to their
simple yet high performance without domain knowledge in sound classification
and localization tasks. However, a lack of gun sounds in existing datasets has
been a major obstacle to implementing a support system to spot criminals from
their gunshots by leveraging deep learning models. Since the occurrence of
gunshot is rare and unpredictable, it is impractical to collect gun sounds in
the real world. As an alternative, gun sounds can be obtained from an FPS game
that is designed to mimic real-world warfare. The recent FPS game offers a
realistic environment where we can safely collect gunshot data while simulating
even dangerous situations. By exploiting the advantage of the game environment,
we construct a gunshot dataset, namely BGG, for the firearm classification and
gunshot localization tasks. The BGG dataset consists of 37 different types of
firearms, distances, and directions between the sound source and a receiver. We
carefully verify that the in-game gunshot data has sufficient information to
identify the location and type of gunshots by training several sound
classification and localization baselines on the BGG dataset. Afterward, we
demonstrate that the accuracy of real-world firearm classification and
localization tasks can be enhanced by utilizing the BGG dataset.

本文介绍了如何利用 FPS 游戏构建一个包含 37 种不同类型枪声、距离和方向的枪声数据集 BGG，有助于训练并优化基于深度学习模型的枪声分类和定位任务，提高实际场景中火器分类和定位的准确度。