{"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/learning-a-representation-for-cover-song","title":"Learning a Representation for Cover Song Identification Using Convolutional Neural Network","arxiv_id":"1911.00334","date":"2019-11-01","proceeding":"arXiv 2019 11","authors":["Zhesong Yu","Xiaoshuo Xu","Xiaoou Chen","Deshun Yang"],"abstract":"Cover song identification represents a challenging task in the field of Music Information Retrieval (MIR) due to complex musical variations between query tracks and cover versions. Previous works typically utilize hand-crafted features and alignment algorithms for the task. More recently, further breakthroughs are achieved employing neural network approaches. In this paper, we propose a novel Convolutional Neural Network (CNN) architecture based on the characteristics of the cover song task. We first train the network through classification strategies; the network is then used to extract music representation for cover song identification. A scheme is designed to train robust models against tempo changes. Experimental results show that our approach outperforms state-of-the-art methods on all public datasets, improving the performance especially on the large dataset.","url_abs":"https://arxiv.org/abs/1911.00334v1","url_pdf":"https://arxiv.org/pdf/1911.00334v1.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":"learning-a-representation-for-cover-song","repo_url":"https://github.com/Orfium/bytecover","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"learning-a-representation-for-cover-song","repo_url":"https://github.com/yzspku/CQTNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cover-song-identification","task_name":"Cover song identification"},{"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":[{"leaderboard":"/sota/cover-song-identification-on-covers80","task":"Cover song identification","dataset":"Covers80","model":"CQT-Net","rank_in_archive_order":5,"of":5,"metrics":{"MAP":"0.840"},"uses_additional_data":false},{"leaderboard":"/sota/cover-song-identification-on-shs100k-test","task":"Cover song identification","dataset":"SHS100K-TEST","model":"CQT-Net","rank_in_archive_order":8,"of":8,"metrics":{"mAP":"0.655"},"uses_additional_data":false},{"leaderboard":"/sota/cover-song-identification-on-youtube350","task":"Cover song identification","dataset":"YouTube350","model":"CQT-Net","rank_in_archive_order":3,"of":4,"metrics":{"MAP":"0.917"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}