论文标题
音乐词嵌入：弥合听力环境和音乐之间的差距
Musical Word Embedding: Bridging the Gap between Listening Contexts and Music
论文作者
论文摘要
Mikolov等人开创的单词嵌入。是自然语言处理（NLP）研究中单词表示形式的主食技术，该研究还发现音乐信息检索任务中的知名度。但是，根据单词嵌入的文本数据类型，词汇大小和音乐相关程度可能会有所不同。在这项工作中，我们（1）使用一般文本数据和特定于音乐的数据的组合训练单词的分布式表示，以及（2）根据他们如何将听力上下文与音乐作品联系起来的系统。
Word embedding pioneered by Mikolov et al. is a staple technique for word representations in natural language processing (NLP) research which has also found popularity in music information retrieval tasks. Depending on the type of text data for word embedding, however, vocabulary size and the degree of musical pertinence can significantly vary. In this work, we (1) train the distributed representation of words using combinations of both general text data and music-specific data and (2) evaluate the system in terms of how they associate listening contexts with musical compositions.
