  This paper presents a task of audio-visual scene classification (SC) where
input videos are classified into one of five real-life crowded scenes: 'Riot',
'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'. To
this end, we firstly collect an audio-visual dataset (videos) of these five
crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep
learning frameworks are proposed to deploy either audio or visual input data
independently. Finally, results obtained from high-performed deep learning
frameworks are fused to achieve the best accuracy score. Our experimental
results indicate that audio and visual input factors independently contribute
to the SC task's performance. Significantly, an ensemble of deep learning
frameworks exploring either audio or visual input data can achieve the best
accuracy of 95.7%.
