  Most recent work in visual sound source localization relies on semantic
audio-visual representations learned in a self-supervised manner, and by design
excludes temporal information present in videos. While it proves to be
effective for widely used benchmark datasets, the method falls short for
challenging scenarios like urban traffic. This work introduces temporal context
into the state-of-the-art methods for sound source localization in urban scenes
using optical flow as a means to encode motion information. An analysis of the
strengths and weaknesses of our methods helps us better understand the problem
of visual sound source localization and sheds light on open challenges for
audio-visual scene understanding.
