{"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-genre-classification-using-masked","title":"Music Genre Classification using Masked Conditional Neural Networks","arxiv_id":"1802.06432","date":"2018-02-18","proceeding":null,"authors":["Fady Medhat","David Chesmore","John Robinson"],"abstract":"The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural\nNetworks (MCLNN) exploit the nature of multi-dimensional temporal signals. The\nCLNN captures the conditional temporal influence between the frames in a window\nand the mask in the MCLNN enforces a systematic sparseness that follows a\nfilterbank-like pattern over the network links. The mask induces the network to\nlearn about time-frequency representations in bands, allowing the network to\nsustain frequency shifts. Additionally, the mask in the MCLNN automates the\nexploration of a range of feature combinations, usually done through an\nexhaustive manual search. We have evaluated the MCLNN performance using the\nBallroom and Homburg datasets of music genres. MCLNN has achieved accuracies\nthat are competitive to state-of-the-art handcrafted attempts in addition to\nmodels based on Convolutional Neural Networks.","url_abs":"http://arxiv.org/abs/1802.06432v2","url_pdf":"http://arxiv.org/pdf/1802.06432v2.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-genre-classification-using-masked","repo_url":"https://github.com/fadymedhat/MCLNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}