# Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification

arXiv: https://arxiv.org/abs/1911.03626 · HTML: https://mlsanity.com/paper/1911.03626/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce](https://mlsanity.com/paper/2408.01928/) (2024)
- [A Semi-supervised Scalable Unified Framework for E-commerce Query Classification](https://mlsanity.com/paper/2506.21049/) (2025)

## Abstract (arXiv)

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.