Descriptor: Certus Caliber Classification Gunshot Dataset (C3GD)

Sinclair Gurny, Ryan Quinn
6/16/2026
cs.SDcs.AI

No confident code implementation found for this paper.

Abstract

In this work, we introduce the Certus Caliber Classification Gunshot Dataset (C3GD), a publicly accessible data set developed for the analysis of firearm muzzle blast sounds. The dataset aims to provide a wide variety of firearms, calibers, cartridges, microphones, and microphone locations with metadata detailed beyond what is currently otherwise available. It comprises more than 8000 field-collected data points from 28 firearms across 16 calibers. Because data collection in the field is costly, much of the existing research has been done using gunshot audio collected from the internet, which increases the risk of low-quality data and label noise. This dataset is primarily focused on caliber classification, but can also be used for gunshot detection, audio separation, and audio signal processing, providing a diversified and real-world reference. The dataset aims to provide enough diversity to be able to generalize to more real-world applications while also providing enough metadata for detailed academic analysis.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{gurny2026descriptor,
  title  = {Descriptor: Certus Caliber Classification Gunshot Dataset (C3GD)},
  author = {Sinclair Gurny and Ryan Quinn},
  year   = {2026},
  eprint = {2606.18135},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.18135v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2606.18135.svg)](https://codeofpaper.com/paper/2606.18135)

Discussion