# Deciphering GunType Hierarchy through Acoustic Analysis of Gunshot Recordings

arXiv: https://arxiv.org/abs/2506.20609 · HTML: https://mlsanity.com/paper/2506.20609/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit](https://mlsanity.com/paper/1605.06778/) (2016)
- [Audio Event Detection using Weakly Labeled Data](https://mlsanity.com/paper/1605.02401/) (2016)
- [A Closer Look at Weak Label Learning for Audio Events](https://mlsanity.com/paper/1804.09288/) (2018)
- [Sound of Guns: Digital Forensics of Gun Audio Samples meets Artificial Intelligence](https://mlsanity.com/paper/2004.07948/) (2020)
- [Learning Sound Events From Webly Labeled Data](https://mlsanity.com/paper/1811.09967/) (2018)
- [Technical Report of the Video Event Reconstruction and Analysis (VERA) System -- Shooter Localization, Models, Interface, and Beyond](https://mlsanity.com/paper/1905.13313/) (2019)
- [Importance of negative sampling in weak label learning](https://mlsanity.com/paper/2309.13227/) (2023)

## Built on by (most cited first, 2 of 2)

- [Descriptor: Certus Caliber Classification Gunshot Dataset (C3GD)](https://mlsanity.com/paper/2606.18135/) (2026)
- [Exploring Feature Extraction Technique Parameters for Acoustic Gunshot Classification](https://mlsanity.com/paper/2606.19568/) (2026)

## Abstract (arXiv)

The escalating rates of gun-related violence and mass shootings represent a significant threat to public safety. Timely and accurate information for law enforcement agencies is crucial in mitigating these incidents. Current commercial gunshot detection systems, while effective, often come with prohibitive costs. This research explores a cost-effective alternative by leveraging acoustic analysis of gunshot recordings, potentially obtainable from ubiquitous devices like cell phones, to not only detect gunshots but also classify the type of firearm used. This paper details a study on deciphering gun type hierarchies using a curated dataset of 3459 recordings. We investigate the fundamental acoustic characteristics of gunshots, including muzzle blasts and shockwaves, which vary based on firearm type, ammunition, and shooting direction. We propose and evaluate machine learning frameworks, including Support Vector Machines (SVMs) as a baseline and a more advanced Convolutional Neural Network (CNN) architecture for joint gunshot detection and gun type classification. Results indicate that our deep learning approach achieves a mean average precision (mAP) of 0.58 on clean labeled data, outperforming the SVM baseline (mAP 0.39). Challenges related to data quality, environmental noise, and the generalization capabilities when using noisy web-sourced data (mAP 0.35) are also discussed. The long-term vision is to develop a highly accurate, real-time system deployable on common recording devices, significantly reducing detection costs and providing critical intelligence to first responders.