{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classification-of-simulated-radio-signals","title":"Classification of simulated radio signals using Wide Residual Networks for use in the search for extra-terrestrial intelligence","arxiv_id":"1803.08624","date":"2018-03-23","proceeding":null,"authors":["G. A. Cox","S. Egly","G. R. Harp","J. Richards","S. Vinodababu","J. Voien"],"abstract":"We describe a new approach and algorithm for the detection of artificial\nsignals and their classification in the search for extraterrestrial\nintelligence (SETI). The characteristics of radio signals observed during SETI\nresearch are often most apparent when those signals are represented as\nspectrograms. Additionally, many observed signals tend to share the same\ncharacteristics, allowing for sorting of the signals into different classes.\nFor this work, complex-valued time-series data were simulated to produce a\ncorpus of 140,000 signals from seven different signal classes. A wide residual\nneural network was then trained to classify these signal types using the\ngray-scale 2D spectrogram representation of those signals. An average $F_1$\nscore of 95.11\\% was attained when tested on previously unobserved simulated\nsignals. We also report on the performance of the model across a range of\nsignal amplitudes.","url_abs":"http://arxiv.org/abs/1803.08624v1","url_pdf":"http://arxiv.org/pdf/1803.08624v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classification-of-simulated-radio-signals","repo_url":"https://github.com/biswajitpawl/radio-signal-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}