{"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/automatic-classification-of-music-genre-using","title":"Automatic Classification of Music Genre using Masked Conditional Neural Networks","arxiv_id":"1801.05504","date":"2018-01-16","proceeding":null,"authors":["Fady Medhat","David Chesmore","John Robinson"],"abstract":"Neural network based architectures used for sound recognition are usually\nadapted from other application domains such as image recognition, which may not\nharness the time-frequency representation of a signal. The ConditionaL Neural\nNetworks (CLNN) and its extension the Masked ConditionaL Neural Networks\n(MCLNN) are designed for multidimensional temporal signal recognition. The CLNN\nis trained over a window of frames to preserve the inter-frame relation, and\nthe MCLNN enforces a systematic sparseness over the network's links that mimics\na filterbank-like behavior. The masking operation induces the network to learn\nin frequency bands, which decreases the network susceptibility to\nfrequency-shifts in time-frequency representations. Additionally, the mask\nallows an exploration of a range of feature combinations concurrently analogous\nto the manual handcrafting of the optimum collection of features for a\nrecognition task. MCLNN have achieved competitive performance on the Ballroom\nmusic dataset compared to several hand-crafted attempts and outperformed models\nbased on state-of-the-art Convolutional Neural Networks.","url_abs":"http://arxiv.org/abs/1801.05504v2","url_pdf":"http://arxiv.org/pdf/1801.05504v2.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":"automatic-classification-of-music-genre-using","repo_url":"https://github.com/fadymedhat/MCLNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General 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}