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

Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence