# Wideband Signal Localization with Spectral Segmentation

arXiv: https://arxiv.org/abs/2110.00583 · HTML: https://mlsanity.com/paper/2110.00583/

## Key facts

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

## Built on by (most cited first, 1 of 1)

- [Large Scale Radio Frequency Wideband Signal Detection & Recognition](https://mlsanity.com/paper/2211.10335/) (2022)

## Abstract (arXiv)

Signal localization is a spectrum sensing problem that jointly detects the presence of a signal and estimates a center frequency and bandwidth. This is a step beyond most spectrum sensing work which estimates "present" or "not present" detections for either a single channel or fixed sized channels. We define the signal localization task, present the metrics of precision and recall, and establish baselines for traditional energy detection on this task. We introduce a new dataset that is useful for training neural networks to perform this task and show a training framework to train signal detectors to achieve the task and present precision and recall curves over SNR. This neural network based approach shows an 8 dB improvement in recall over the traditional energy detection approach with minor improvements in precision.