  With the explosive increase of User Generated Content (UGC), UGC video
quality assessment (VQA) becomes more and more important for improving users'
Quality of Experience (QoE). However, most existing UGC VQA studies only focus
on the visual distortions of videos, ignoring that the user's QoE also depends
on the accompanying audio signals. In this paper, we conduct the first study to
address the problem of UGC audio and video quality assessment (AVQA).
Specifically, we construct the first UGC AVQA database named the SJTU-UAV
database, which includes 520 in-the-wild UGC audio and video (A/V) sequences,
and conduct a user study to obtain the mean opinion scores of the A/V
sequences. The content of the SJTU-UAV database is then analyzed from both the
audio and video aspects to show the database characteristics. We also design a
family of AVQA models, which fuse the popular VQA methods and audio features
via support vector regressor (SVR). We validate the effectiveness of the
proposed models on the three databases. The experimental results show that with
the help of audio signals, the VQA models can evaluate the perceptual quality
more accurately. The database will be released to facilitate further research.
