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.

翻译：近年来，基于深度学习的方法因其简洁高效且无需领域知识即可在声音分类与定位任务中取得卓越性能而备受关注。然而，现有数据集中枪声数据的匮乏，已成为利用深度学习模型通过枪声识别犯罪分子这一支持系统实施的主要障碍。由于真实枪击事件罕见且不可预测，在现实世界中收集枪声数据并不可行。作为一种替代方案，可从模拟真实战场环境的第一人称射击游戏（FPS）中获取枪声数据。当前FPS游戏提供了可安全收集枪声数据、甚至能模拟危险场景的逼真环境。利用游戏环境的优势，我们构建了一个名为BGG的枪声数据集，用于枪支分类与枪声定位任务。该数据集包含37种不同枪支类型、声音源与接收器之间的多类距离与方向信息。通过在BGG数据集上训练多种声音分类与定位基线模型，我们严格验证了游戏内枪声数据具备识别枪声位置与类型的充分信息。随后，我们证明了利用BGG数据集可提升真实场景下枪支分类与枪声定位任务的准确性。