# A Wideband Signal Recognition Dataset

arXiv: https://arxiv.org/abs/2110.00518 · HTML: https://mlsanity.com/paper/2110.00518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

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

- [Joint Signal Detection and Automatic Modulation Classification via Deep Learning](https://mlsanity.com/paper/2405.00736/) (2024)
- [Large Scale Radio Frequency Wideband Signal Detection & Recognition](https://mlsanity.com/paper/2211.10335/) (2022)

## Abstract (arXiv)

Signal recognition is a spectrum sensing problem that jointly requires detection, localization in time and frequency, and classification. This is a step beyond most spectrum sensing work which involves signal detection to estimate "present" or "not present" detections for either a single channel or fixed sized channels or classification which assumes a signal is present. We define the signal recognition task, present the metrics of precision and recall to the RF domain, and review recent machine-learning based approaches to this problem. We introduce a new dataset that is useful for training neural networks to perform these tasks and show a training framework to train wideband signal recognizers.