  Audio classification is considered as a challenging problem in pattern
recognition. Recently, many algorithms have been proposed using deep neural
networks. In this paper, we introduce a new attention-based neural network
architecture called Classifier-Attention-Based Convolutional Neural Network
(CAB-CNN). The algorithm uses a newly designed architecture consisting of a
list of simple classifiers and an attention mechanism as a classifier selector.
This design significantly reduces the number of parameters required by the
classifiers and thus their complexities. In this way, it becomes easier to
train the classifiers and achieve a high and steady performance. Our claims are
corroborated by the experimental results. Compared to the state-of-the-art
algorithms, our algorithm achieves more than 10% improvements on all selected
test scores.
