Music Auto-Tagging with Capsule Network

In recent years, convolutional neural networks (CNNs) become popular approaches used in music information retrieval (MIR) tasks, such as mood recognition, music auto-tagging and so on. Since CNNs are able to extract the local features effectively, previous attempts show great performance on music auto-tagging. However, CNNs is not able to capture the spatial features and the relationship between low-level features are neglected. Motivated by this problem, a hybrid architecture is proposed based on Capsule Network, which is capable to extract spatial features with the routing-by-agreement mechanism. The proposed model was applied in music auto-tagging. The results show that it achieves promising results of the ROC-AUC score of 90.67%.