Paper Info
Title
Representation Learning of Music Using Artist, Album, and Track Information.
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.
Year
Venue
DocType
2019
CoRR
Journal
Volume
Citations 
PageRank 
abs/1906.11783
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jongpil Lee111115.79
Jiyoung Park2193.51
Juhan Nam326125.12