# Representation Learning of Music Using Artist Labels

arXiv: https://arxiv.org/abs/1710.06648 · HTML: https://mlsanity.com/paper/1710.06648/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [Learning music audio representations via weak language supervision](https://mlsanity.com/paper/2112.04214/) (2021)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Unsupervised Musical Object Discovery from Audio](https://mlsanity.com/paper/2311.07534/) (2023)
- [CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining](https://mlsanity.com/paper/2503.23128/) (2025)
- [General Item Representation Learning for Cold-start Content Recommendations](https://mlsanity.com/paper/2404.13808/) (2024)
- [Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks](https://mlsanity.com/paper/2409.09026/) (2024)

## Abstract (arXiv)

In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data. We propose two deep convolutional neural networks (DCNN) to learn the deep artist features. One is a plain DCNN trained with the whole artist labels simultaneously, and the other is a Siamese DCNN trained with a subset of the artist labels based on the artist identity. We apply the trained models to music classification and retrieval tasks in transfer learning settings. The results show that our approach is comparable to previous state-of-the-art methods, indicating that the proposed approach captures general music audio features as much as the models learned with semantic labels. Also, we discuss the advantages and disadvantages of the two models.