  We introduce a powerful recurrent neural network based method for novelty
detection to the application of detecting radio anomalies. This approach holds
promise in significantly increasing the ability of naive anomaly detection to
detect small anomalies in highly complex complexity multi-user radio bands. We
demonstrate the efficacy of this approach on a number of common real over the
air radio communications bands of interest and quantify detection performance
in terms of probability of detection an false alarm rates across a range of
interference to band power ratios and compare to baseline methods.
