  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.
