{"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/masked-conditional-neural-networks-for","title":"Masked Conditional Neural Networks for Automatic Sound Events Recognition","arxiv_id":"1802.05792","date":"2018-02-15","proceeding":null,"authors":["Fady Medhat","David Chesmore","John Robinson"],"abstract":"Deep neural network architectures designed for application domains other than\nsound, especially image recognition, may not optimally harness the\ntime-frequency representation when adapted to the sound recognition problem. In\nthis work, we explore the ConditionaL Neural Network (CLNN) and the Masked\nConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal\nrecognition. The CLNN considers the inter-frame relationship, and the MCLNN\nenforces a systematic sparseness over the network's links to enable learning in\nfrequency bands rather than bins allowing the network to be frequency shift\ninvariant mimicking a filterbank. The mask also allows considering several\ncombinations of features concurrently, which is usually handcrafted through\nexhaustive manual search. We applied the MCLNN to the environmental sound\nrecognition problem using the ESC-10 and ESC-50 datasets. MCLNN achieved\ncompetitive performance, using 12% of the parameters and without augmentation,\ncompared to state-of-the-art Convolutional Neural Networks.","url_abs":"http://arxiv.org/abs/1802.05792v2","url_pdf":"http://arxiv.org/pdf/1802.05792v2.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":"masked-conditional-neural-networks-for","repo_url":"https://github.com/fadymedhat/MCLNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"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}