  As we interact with the world, for example when we communicate with our
colleagues in a large open space or meeting room, we continuously analyse the
surrounding environment and, in particular, localise and recognise acoustic
events. While we largely take such abilities for granted, they represent a
challenging problem for current robots or smart voice assistants as they can be
easily fooled by high degree of sound interference in acoustically complex
environments. Preventing such failures when using solely audio data is
challenging, if not impossible since the algorithms need to take into account
wider context and often understand the scene on a semantic level. In this
paper, we propose what to our knowledge is the first multi-modal direction of
arrival (DoA) of sound, which uses static visual spatial prior providing an
auxiliary information about the environment to suppress some of the false DoA
detections. We validate our approach on a newly collected real-world dataset,
and show that our approach consistently improves over classic DoA baselines
