Musical Word Embedding: Bridging the Gap between Listening Contexts and Music

• URL: http://arxiv.org/abs/2008.01190v1
• Date: Thu, 23 Jul 2020 06:42:45 GMT
• Title: Musical Word Embedding: Bridging the Gap between Listening Contexts and Music
• Authors: Seungheon Doh, Jongpil Lee, Tae Hong Park, Juhan Nam
• Abstract summary: We train the distributed representation of words using combinations of both general text data and music-specific data. We evaluate the system in terms of how they associate listening contexts with musical compositions.
• Score: 5.89179309980335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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