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As we interact with the world, for example when we communicate with ourcolleagues in a large open space or meeting room, we continuously analyse thesurrounding environment and, in particular, localise and recognise acousticevents. While we largely take such abilities for granted, they represent achallenging problem for current robots or smart voice assistants as they can beeasily fooled by high degree of sound interference in acoustically complexenvironments. Preventing such failures when using solely audio data ischallenging, if not impossible since the algorithms need to take into accountwider context and often understand the scene on a semantic level. In thispaper, we propose what to our knowledge is the first multi-modal direction ofarrival (DoA) of sound, which uses static visual spatial prior providing anauxiliary information about the environment to suppress some of the false DoAdetections. We validate our approach on a newly collected real-world dataset,and show that our approach consistently improves over classic DoA baselines

Static Visual Spatial Priors for DoA Estimation