用句子嵌入实现跨语言多标签音乐流派分类
Multi-label Cross-lingual automatic music genre classification from lyrics with Sentence BERT
- 基于sBERT的多语言句向量进行跨语言流派识别
- 跨语言准确率提升，单流派平均F1达0.69
- 适合处理小语种音乐数据，支持多标签预测
音乐流派由歌曲风格特征和艺术家受众文化偏好共同塑造。利用歌词自动分类音乐流派在推荐系统、歌单生成和图书馆管理中有重要应用。本文提出一种基于多语言句向量（sBERT）的多标签跨语言分类系统。使用包含八种重叠流派的葡英双语数据集，证明了该系统可在一种语言上训练，对另一种语言的歌词进行流派预测。相比翻译歌词后使用词袋表示的基线方法，本方法将流派级平均F1分数从0.35提升至0.69。分类器采用一对多架构，可为单条歌词分配多个流派标签。实验表明，数据集中化显著提升跨语言性能。该方法为非主流语言和文化领域的流派分类提供了可扩展解决方案，推动了音乐信息检索技术的发展。
原文摘要 · Abstract (English)
Music genres are shaped by both the stylistic features of songs and the cultural preferences of artists' audiences. Automatic classification of music genres using lyrics can be useful in several applications such as recommendation systems, playlist creation, and library organization. We present a multi-label, cross-lingual genre classification system based on multilingual sentence embeddings generated by sBERT. Using a bilingual Portuguese-English dataset with eight overlapping genres, we demonstrate the system's ability to train on lyrics in one language and predict genres in another. Our approach outperforms the baseline approach of translating lyrics and using a bag-of-words representation, improving the genrewise average F1-Score from 0.35 to 0.69. The classifier uses a one-vs-all architecture, enabling it to assign multiple genre labels to a single lyric. Experimental results reveal that dataset centralization notably improves cross-lingual performance. This approach offers a scalable solution for genre classification across underrepresented languages and cultural domains, advancing the capabilities of music information retrieval systems.
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