论文标题
基于BLSTM网络的基于巴西歌词的音乐类型分类
Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network
论文作者
论文摘要
可以在类型标签的帮助下以共同相似性来组织歌曲，专辑和艺术家。在本文中，我们介绍了一种新颖的方法，用于仅使用歌曲歌词在巴西音乐中自动分类音乐类型。在自然语言处理领域，这种分类仍然是一个挑战。我们构建了一个138,368个巴西歌曲歌词的数据集，该歌词以14种流派分发。我们应用SVM，随机森林和双向长期记忆（BLSTM）网络，结合了不同的单词嵌入技术来解决此分类任务。我们的实验表明，BLSTM方法的表现优于其他模型，F1得分平均值为0.48美元。分别在巴西音乐类型的环境中，分别将“福音”，“福音”，“ Funk-Carioca”和“ Sertanejo”（分别获得F1得分的0.89、0.70和0.69）定义为最独特，最易于分类的类型。
Organize songs, albums, and artists in groups with shared similarity could be done with the help of genre labels. In this paper, we present a novel approach for automatic classifying musical genre in Brazilian music using only the song lyrics. This kind of classification remains a challenge in the field of Natural Language Processing. We construct a dataset of 138,368 Brazilian song lyrics distributed in 14 genres. We apply SVM, Random Forest and a Bidirectional Long Short-Term Memory (BLSTM) network combined with different word embeddings techniques to address this classification task. Our experiments show that the BLSTM method outperforms the other models with an F1-score average of $0.48$. Some genres like "gospel", "funk-carioca" and "sertanejo", which obtained 0.89, 0.70 and 0.69 of F1-score, respectively, can be defined as the most distinct and easy to classify in the Brazilian musical genres context.
