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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.","url_abs":"http://arxiv.org/abs/1710.06648v2","url_pdf":"http://arxiv.org/pdf/1710.06648v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"representation-learning-of-music-using-artist","repo_url":"https://github.com/jiyoungpark527/msd-artist-split","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"representation-learning-of-music-using-artist","repo_url":"https://github.com/jongpillee/ismir2018-artist","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.06648","atlas_url":"https://app.syntology.ai/?focus=1710.06648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.06648"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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