The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.

本研究旨在探究如何通过使用多语义表示、分布式概念嵌入和本体论等技术，实现在不同语境和文化背景下的音乐类型交叉注释的目标，并在该领域中提出了一个新的、领域特定的用于跨领域语料库的基准。研究结果指出：该方法能够实现高准确率的无监督跨语言音乐流派注释。

跨语言文化中音乐流派感知建模