{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/music-artist-classification-with","title":"Music Artist Classification with Convolutional Recurrent Neural Networks","arxiv_id":"1901.04555","date":"2019-01-14","proceeding":null,"authors":["Zain Nasrullah","Yue Zhao"],"abstract":"Previous attempts at music artist classification use frame level audio\nfeatures which summarize frequency content within short intervals of time.\nComparatively, more recent music information retrieval tasks take advantage of\ntemporal structure in audio spectrograms using deep convolutional and recurrent\nmodels. This paper revisits artist classification with this new framework and\nempirically explores the impacts of incorporating temporal structure in the\nfeature representation. To this end, an established classification\narchitecture, a Convolutional Recurrent Neural Network (CRNN), is applied to\nthe artist20 music artist identification dataset under a comprehensive set of\nconditions. These include audio clip length, which is a novel contribution in\nthis work, and previously identified considerations such as dataset split and\nfeature level. Our results improve upon baseline works, verify the influence of\nthe producer effect on classification performance and demonstrate the\ntrade-offs between audio length and training set size. The best performing\nmodel achieves an average F1 score of 0.937 across three independent trials\nwhich is a substantial improvement over the corresponding baseline under\nsimilar conditions. Additionally, to showcase the effectiveness of the CRNN's\nfeature extraction capabilities, we visualize audio samples at the model's\nbottleneck layer demonstrating that learned representations segment into\nclusters belonging to their respective artists.","url_abs":"http://arxiv.org/abs/1901.04555v2","url_pdf":"http://arxiv.org/pdf/1901.04555v2.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":"abstracts"},"code_links":[{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/ZainNasrullah/music-artist-classification-crnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/AleksSol/music_artist_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/hafezgh/Music_genre_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/winstonll/SynC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/winstonll/Synthetic_Population","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"music-artist-classification-with","repo_url":"https://github.com/MindSpore-scientific-2/code-10/tree/main/text-to-music","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"artist-classification","task_name":"Artist classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"music-information-retrieval","task_name":"Music Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}