{"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/bottom-up-broadcast-neural-network-for-music","title":"Bottom-up Broadcast Neural Network For Music Genre Classification","arxiv_id":"1901.08928","date":"2019-01-24","proceeding":null,"authors":["Caifeng Liu","Lin Feng","Guochao Liu","Huibing Wang","Shenglan Liu"],"abstract":"Music genre recognition based on visual representation has been successfully\nexplored over the last years. Recently, there has been increasing interest in\nattempting convolutional neural networks (CNNs) to achieve the task. However,\nmost of existing methods employ the mature CNN structures proposed in image\nrecognition without any modification, which results in the learning features\nthat are not adequate for music genre classification. Faced with the challenge\nof this issue, we fully exploit the low-level information from spectrograms of\naudios and develop a novel CNN architecture in this paper. The proposed CNN\narchitecture takes the long contextual information into considerations, which\ntransfers more suitable information for the decision-making layer. Various\nexperiments on several benchmark datasets, including GTZAN, Ballroom, and\nExtended Ballroom, have verified the excellent performances of the proposed\nneural network. Codes and model will be available at\n\"ttps://github.com/CaifengLiu/music-genre-classification\".","url_abs":"http://arxiv.org/abs/1901.08928v1","url_pdf":"http://arxiv.org/pdf/1901.08928v1.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":"bottom-up-broadcast-neural-network-for-music","repo_url":"https://github.com/CaifengLiu/music-genre-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"genre-classification","task_name":"Genre classification"},{"task_slug":"music-genre-classification","task_name":"Music Genre Classification"},{"task_slug":"music-genre-recognition","task_name":"Music Genre Recognition"}],"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}