Music genre classification, especially using lyrics alone, remains a
challenging topic in Music Information Retrieval. In this study we apply
recurrent neural network models to classify a large dataset of intact song
lyrics. As lyrics exhibit a hierarchical layer structure - in which words
combine to form lines, lines form segments, and segments form a complete song -
we adapt a hierarchical attention network (HAN) to exploit these layers and in
addition learn the importance of the words, lines, and segments. We test the
model over a 117-genre dataset and a reduced 20-genre dataset. Experimental
results show that the HAN outperforms both non-neural models and simpler neural
models, whilst also classifying over a higher number of genres than previous
research. Through the learning process we can also visualise which words or
lines in a song the model believes are important to classifying the genre. As a
result the HAN provides insights, from a computational perspective, into
lyrical structure and language features that differentiate musical genres.

本文使用循环神经网络模型对大规模的歌曲歌词数据集进行音乐流派分类，并在此基础上利用分层注意力网络（HAN）实现在不同层级的特征学习以分类音乐流派，该方法不仅在准确率上超过了先前的研究成果，而且还提供了洞察在语言特征和词汇结构方面不同的音乐流派之间的区别。