Representation Learning of Music Using Artist, Album, and Track Information
Information Retrieval
2019-06-28 v1 Multimedia
Sound
Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.1906.11783,
title = {Representation Learning of Music Using Artist, Album, and Track Information},
author = {Jongpil Lee and Jiyoung Park and Juhan Nam},
journal= {arXiv preprint arXiv:1906.11783},
year = {2019}
}
Comments
International Conference on Machine Learning (ICML) 2019, Machine Learning for Music Discovery Workshop