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Enemy Spotted: in-game gun sound dataset for gunshot classification and localization by Park, Junwoo; Cho, Youngwoo; Sim, Gyuhyeon; Lee, Hojoon; Choo, Jaegul is a scholarly article available to read on EtoBox.

What is Enemy Spotted: in-game gun sound dataset for gunshot classification and localization about?

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 baselin

Author

Park, Junwoo; Cho, Youngwoo; Sim, Gyuhyeon; Lee, Hojoon; Choo, Jaegul

Published

2022

Language

EN