{"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/sample-level-cnn-architectures-for-music-auto","title":"Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms","arxiv_id":"1710.10451","date":"2017-10-28","proceeding":null,"authors":["Taejun Kim","Jongpil Lee","Juhan Nam"],"abstract":"Recent work has shown that the end-to-end approach using convolutional neural\nnetwork (CNN) is effective in various types of machine learning tasks. For\naudio signals, the approach takes raw waveforms as input using an 1-D\nconvolution layer. In this paper, we improve the 1-D CNN architecture for music\nauto-tagging by adopting building blocks from state-of-the-art image\nclassification models, ResNets and SENets, and adding multi-level feature\naggregation to it. We compare different combinations of the modules in building\nCNN architectures. The results show that they achieve significant improvements\nover previous state-of-the-art models on the MagnaTagATune dataset and\ncomparable results on Million Song Dataset. Furthermore, we analyze and\nvisualize our model to show how the 1-D CNN operates.","url_abs":"http://arxiv.org/abs/1710.10451v2","url_pdf":"http://arxiv.org/pdf/1710.10451v2.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":"sample-level-cnn-architectures-for-music-auto","repo_url":"https://github.com/tae-jun/resemul","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"sample-level-cnn-architectures-for-music-auto","repo_url":"https://github.com/jaehwlee/tf2-music-tagging-models","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"music-auto-tagging","task_name":"Music Auto-Tagging"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}