  Automatically labeling multiple styles for every song is a comprehensive
application in all kinds of music websites. Recently, some researches explore
review-driven multi-label music style classification and exploit style
correlations for this task. However, their methods focus on mining the
statistical relations between different music styles and only consider shallow
style relations. Moreover, these statistical relations suffer from the
underfitting problem because some music styles have little training data.
  To tackle these problems, we propose a novel knowledge relations integrated
framework (KRF) to capture the complete style correlations, which jointly
exploits the inherent relations between music styles according to external
knowledge and their statistical relations. Based on the two types of relations,
we use a graph convolutional network to learn the deep correlations between
styles automatically. Experimental results show that our framework
significantly outperforms state-of-the-art methods. Further studies demonstrate
that our framework can effectively alleviate the underfitting problem and learn
meaningful style correlations. The source code can be available at
https://github.com/Makwen1995/MusicGenre.
