# Representation Learning of Music Using Artist, Album, and Track Information

arXiv: https://arxiv.org/abs/1906.11783 · HTML: https://mlsanity.com/paper/1906.11783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [Learning Music Sequence Representation from Text Supervision](https://mlsanity.com/paper/2305.19602/) (2023)
- [Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song](https://mlsanity.com/paper/2308.02249/) (2023)

## Abstract (arXiv)

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.