  Existing datasets used to train deep learning models for narrowband radio
frequency (RF) signal classification lack enough diversity in signal types and
channel impairments to sufficiently assess model performance in the real world.
We introduce the Sig53 dataset consisting of 5 million synthetically-generated
samples from 53 different signal classes and expertly chosen impairments. We
also introduce TorchSig, a signals processing machine learning toolkit that can
be used to generate this dataset. TorchSig incorporates data handling
principles that are common to the vision domain, and it is meant to serve as an
open-source foundation for future signals machine learning research. Initial
experiments using the Sig53 dataset are conducted using state of the art (SoTA)
convolutional neural networks (ConvNets) and Transformers. These experiments
reveal Transformers outperform ConvNets without the need for additional
regularization or a ConvNet teacher, which is contrary to results from the
vision domain. Additional experiments demonstrate that TorchSig's
domain-specific data augmentations facilitate model training, which ultimately
benefits model performance. Finally, TorchSig supports on-the-fly synthetic
data creation at training time, thus enabling massive scale training sessions
with virtually unlimited datasets.
