Abstract
Early detection of firearm discharge has become increasingly critical for situational awareness in both civilian and military domains. The ability to determine the location and model of a discharged firearm is vital, as this can inform effective response plans. To this end, several gunshot audio datasets have been released that aim to facilitate gunshot detection and classification of a discharged firearm based on acoustic signatures. However, these datasets often suffer from a lack of variety in the orientations of recording devices around the source of the gunshot. Additionally, these datasets often suffer from the absence of proper time synchronization, which prevents the usage of these datasets for determining the Direction of Arrival (DoA) of the sound. In this paper, we present a multi-firearm, multi-orientation time-synchronized audio dataset collected in a semi-controlled real-world setting – providing us a degree of supervision – using several edge devices positioned in and around an outdoor firing range.
| Original language | English |
|---|---|
| Article number | 109091 |
| Journal | Data in Brief |
| Volume | 48 |
| DOIs | |
| State | Published - Jun 2023 |
| Externally published | Yes |
Funding
We would like to thank Eastern Florida State College's range master Frank Ramos and Just Cause Tactical Training manager and instructor Scott Lindsley for their help in building this dataset. This material is based upon work supported by Air Force Research Laboratory (AFRL) grant No. FA8650-21-C-1147. Any opinions, findings, conclusions, or recommendations contained herein are those of the authors and do not necessarily represent the official policies or endorsements, either expressed or implied, of the AFRL or the U.S. Government.
Keywords
- Acoustic situational awareness
- Audio forensics
- Gunshot audio classification
- Internet of Battlefield Things (IoBT)
- Machine learning
- Multiple sensor orchestration