  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.
