  In this paper, we propose two techniques, namely joint modeling and data
augmentation, to improve system performances for audio-visual scene
classification (AVSC). We employ pre-trained networks trained only on image
data sets to extract video embedding; whereas for audio embedding models, we
decide to train them from scratch. We explore different neural network
architectures for joint modeling to effectively combine the video and audio
modalities. Moreover, data augmentation strategies are investigated to increase
audio-visual training set size. For the video modality the effectiveness of
several operations in RandAugment is verified. An audio-video joint mixup
scheme is proposed to further improve AVSC performances. Evaluated on the
development set of TAU Urban Audio Visual Scenes 2021, our final system can
achieve the best accuracy of 94.2% among all single AVSC systems submitted to
DCASE 2021 Task 1b.
