  Recent work has shown that the end-to-end approach using convolutional neural
network (CNN) is effective in various types of machine learning tasks. For
audio signals, the approach takes raw waveforms as input using an 1-D
convolution layer. In this paper, we improve the 1-D CNN architecture for music
auto-tagging by adopting building blocks from state-of-the-art image
classification models, ResNets and SENets, and adding multi-level feature
aggregation to it. We compare different combinations of the modules in building
CNN architectures. The results show that they achieve significant improvements
over previous state-of-the-art models on the MagnaTagATune dataset and
comparable results on Million Song Dataset. Furthermore, we analyze and
visualize our model to show how the 1-D CNN operates.
