Wideband spectral monitoring using deep learning

Abstract

We present a system to perform spectral monitoring of a wide band of 666.5 MHz, located within a range of 6 GHz of Radio Frequency (RF) bandwidth, using state-of-the-art deep learning approaches. The system detects, labels, and localizes in time and frequency signals of interest (SOIs) against a background of wideband RF activity. We apply a hierarchical approach. At the lower level we use a sweeping window to analyze a wideband spectrogram, which is input to a deep convolutional network that estimates local probabilities for the presence of SOIs for each position of the window. In a subsequent, higher-level processing step, these local frame probability estimates are integrated over larger two-dimensional regions that are hypothesized by a second neural network, a region proposal network, adapted from object localization in image processing. The integrated segmental probability scores are used to detect SOIs in the hypothesized spectro-temporal regions.