  Recently, we proposed a self-attention based music tagging model. Different
from most of the conventional deep architectures in music information
retrieval, which use stacked 3x3 filters by treating music spectrograms as
images, the proposed self-attention based model attempted to regard music as a
temporal sequence of individual audio events. Not only the performance, but it
could also facilitate better interpretability. In this paper, we mainly focus
on visualizing and understanding the proposed self-attention based music
tagging model.
