  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.
