Enemy Spotted: in-game gun sound dataset for gunshot classification and localization

TL;DR

This paper introduces BGG, a comprehensive in-game gunshot dataset from FPS games, enabling improved deep learning-based firearm classification and localization, addressing the scarcity of real-world gunshot data.

Contribution

The creation of the BGG dataset from FPS games for firearm classification and localization, and demonstrating its effectiveness in enhancing real-world task accuracy.

Findings

BGG dataset includes 37 firearm types, distances, and directions.

Training on BGG improves real-world gunshot classification accuracy.

In-game data effectively supports firearm localization tasks.

Abstract

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.