基于风格相关性的评论驱动多标签音乐风格分类
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
摘要：
本文探讨了一种新的自然语言处理任务--评论驱动的多标签音乐风格分类。这项任务要求系统根据网站上的评论来识别多种风格的音乐。最大的挑战在于音乐风格之间的复杂关系。它给许多多标签分类方法带来了失败。为了解决这个问题，我们提出了一种新的深度学习方法来自动学习和利用风格相关性。该方法由两部分组成：一个基于标签图的神经网络，和一个软训练机制，基于相关性的连续标签表示。实验结果表明，我们的方法实现了较大的改进，在建议的数据集上的基线。特别是，显微F1从53.9提高到64.5，一次误差从30.5降低到22.6。此外，可视化分析表明，我们的方法在捕捉风格相关性方面表现良好。
英文摘要：
This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music based on its reviews on websites. The biggest challenge lies in the complicated relations of music styles. It has brought failure to many multi-label classification methods. To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations. The proposed method consists of two parts: a label-graph based neural network, and a soft training mechanism with correlation-based continuous label representation. Experimental results show that our approach achieves large improvements over the baselines on the proposed dataset. Especially, the micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5 to 22.6. Furthermore, the visualized analysis shows that our approach performs well in capturing style correlations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
