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

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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.
Publisher
The International Conference on Machine Learning (ICML)
Issue Date
2020-07-18
Language
English
Citation

Machine Learning for Media Discovery Workshop (ML4DL), International Conference on Machine Learning

URI
http://hdl.handle.net/10203/280168
Appears in Collection
GCT-Conference Papers(학술회의논문)
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