  In this paper, we present an approach to Complex Event Processing (CEP) that
is based on DeepProbLog. This approach has the following objectives: (i)
allowing the use of subsymbolic data as an input, (ii) retaining the
flexibility and modularity on the definitions of complex event rules, (iii)
allowing the system to be trained in an end-to-end manner and (iv) being robust
against noisily labelled data. Our approach makes use of DeepProbLog to create
a neuro-symbolic architecture that combines a neural network to process the
subsymbolic data with a probabilistic logic layer to allow the user to define
the rules for the complex events. We demonstrate that our approach is capable
of detecting complex events from an audio stream. We also demonstrate that our
approach is capable of training even with a dataset that has a moderate
proportion of noisy data.
