A multi-firearm, multi-orientation audio dataset of gunshots
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 dat...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235234092300210X |
| _version_ | 1797798084476928000 |
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| author | Ruksana Kabealo Steven Wyatt Akshay Aravamudan Xi Zhang David N. Acaron Mawaba P. Dao David Elliott Anthony O. Smith Carlos E. Otero Luis D. Otero Georgios C. Anagnostopoulos Adrian M. Peter Wesley Jones Eric Lam |
| author_facet | Ruksana Kabealo Steven Wyatt Akshay Aravamudan Xi Zhang David N. Acaron Mawaba P. Dao David Elliott Anthony O. Smith Carlos E. Otero Luis D. Otero Georgios C. Anagnostopoulos Adrian M. Peter Wesley Jones Eric Lam |
| author_sort | Ruksana Kabealo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:58:09Z |
| format | Article |
| id | doaj.art-8a6a23063ba449e882c192d4902dedb6 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-13T03:58:09Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-8a6a23063ba449e882c192d4902dedb62023-06-22T05:03:32ZengElsevierData in Brief2352-34092023-06-0148109091A multi-firearm, multi-orientation audio dataset of gunshotsRuksana Kabealo0Steven Wyatt1Akshay Aravamudan2Xi Zhang3David N. Acaron4Mawaba P. Dao5David Elliott6Anthony O. Smith7Carlos E. Otero8Luis D. Otero9Georgios C. Anagnostopoulos10Adrian M. Peter11Wesley Jones12Eric Lam13Department of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United States; Corresponding author.Department of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesDepartment of Computer Engineering and Sciences, Center for Advanced Data Analytics and Systems, Florida Institute of Technology, Melbourne, FL, 32901, United StatesUnited States Air Force Research Laboratory (AFRL/RYAA), United StatesUnited States Air Force Research Laboratory (AFRL/RYAA), United StatesEarly 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.http://www.sciencedirect.com/science/article/pii/S235234092300210XGunshot audio classificationAudio forensicsMachine learningAcoustic situational awarenessMultiple sensor orchestrationInternet of Battlefield Things (IoBT) |
| spellingShingle | Ruksana Kabealo Steven Wyatt Akshay Aravamudan Xi Zhang David N. Acaron Mawaba P. Dao David Elliott Anthony O. Smith Carlos E. Otero Luis D. Otero Georgios C. Anagnostopoulos Adrian M. Peter Wesley Jones Eric Lam A multi-firearm, multi-orientation audio dataset of gunshots Data in Brief Gunshot audio classification Audio forensics Machine learning Acoustic situational awareness Multiple sensor orchestration Internet of Battlefield Things (IoBT) |
| title | A multi-firearm, multi-orientation audio dataset of gunshots |
| title_full | A multi-firearm, multi-orientation audio dataset of gunshots |
| title_fullStr | A multi-firearm, multi-orientation audio dataset of gunshots |
| title_full_unstemmed | A multi-firearm, multi-orientation audio dataset of gunshots |
| title_short | A multi-firearm, multi-orientation audio dataset of gunshots |
| title_sort | multi firearm multi orientation audio dataset of gunshots |
| topic | Gunshot audio classification Audio forensics Machine learning Acoustic situational awareness Multiple sensor orchestration Internet of Battlefield Things (IoBT) |
| url | http://www.sciencedirect.com/science/article/pii/S235234092300210X |
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