  Transient radio frequency interference (RFI) is detrimental to radio
astronomy. It is particularly difficult to identify the sources of transient
RFI, which is broadband and intermittent. Such RFI is often generated by
devices like mechanical relays, fluorescent lighting or AC machines, which may
be present in the surrounding infrastructure of a radio telescope array. One
mitigating approach is to deploy independent RFI monitoring stations at radio
telescope arrays. Once the sources of RFI signals are identified, they may be
removed or replaced where possible. For the first time in the open literature,
we demonstrate an approach to classifying the sources of transient RFI (in time
domain data) that makes use of deep learning techniques including CNNs and
LSTMs. Applied to a previously obtained dataset of experimentally recorded
transient RFI signals, our proposed approach offers good results. It shows
potential for development into a tool for identifying the sources of transient
RFI signals recorded by independent RFI monitoring stations.
