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Representation Learning of Music Using Artist, Album, and Track Information
June 27, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Jongpil Lee, Jiyoung Park, Juhan Nam
arXiv ID
1906.11783
Category
cs.IR: Information Retrieval
Cross-listed
cs.MM,
cs.SD,
eess.AS
Citations
15
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Supervised music representation learning has been performed mainly using semantic labels such as music genres. However, annotating music with semantic labels requires time and cost. In this work, we investigate the use of factual metadata such as artist, album, and track information, which are naturally annotated to songs, for supervised music representation learning. The results show that each of the metadata has individual concept characteristics, and using them jointly improves overall performance.
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