  We describe a new approach and algorithm for the detection of artificial
signals and their classification in the search for extraterrestrial
intelligence (SETI). The characteristics of radio signals observed during SETI
research are often most apparent when those signals are represented as
spectrograms. Additionally, many observed signals tend to share the same
characteristics, allowing for sorting of the signals into different classes.
For this work, complex-valued time-series data were simulated to produce a
corpus of 140,000 signals from seven different signal classes. A wide residual
neural network was then trained to classify these signal types using the
gray-scale 2D spectrogram representation of those signals. An average $F_1$
score of 95.11\% was attained when tested on previously unobserved simulated
signals. We also report on the performance of the model across a range of
signal amplitudes.
