  In this paper, we present deep learning frameworks for audio-visual scene
classification (SC) and indicate how individual visual and audio features as
well as their combination affect SC performance. Our extensive experiments,
which are conducted on DCASE (IEEE AASP Challenge on Detection and
Classification of Acoustic Scenes and Events) Task 1B development dataset,
achieve the best classification accuracy of 82.2%, 91.1%, and 93.9% with audio
input only, visual input only, and both audio-visual input, respectively. The
highest classification accuracy of 93.9%, obtained from an ensemble of
audio-based and visual-based frameworks, shows an improvement of 16.5% compared
with DCASE baseline.
