  Audio scene classification, the problem of predicting class labels of audio
scenes, has drawn lots of attention during the last several years. However, it
remains challenging and falls short of accuracy and efficiency. Recently,
Convolutional Neural Network (CNN)-based methods have achieved better
performance with comparison to the traditional methods. Nevertheless,
conventional single channel CNN may fail to consider the fact that additional
cues may be embedded in the multi-channel recordings. In this paper, we explore
the use of Multi-channel CNN for the classification task, which aims to extract
features from different channels in an end-to-end manner. We conduct the
evaluation compared with the conventional CNN and traditional Gaussian Mixture
Model-based methods. Moreover, to improve the classification accuracy further,
this paper explores the using of mixup method. In brief, mixup trains the
neural network on linear combinations of pairs of the representation of audio
scene examples and their labels. By employing the mixup approach for data
argumentation, the novel model can provide higher prediction accuracy and
robustness in contrast with previous models, while the generalization error can
also be reduced on the evaluation data.
