{"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/environmental-sound-recognition-using-masked","title":"Environmental Sound Recognition using Masked Conditional Neural Networks","arxiv_id":"1804.02665","date":"2018-04-08","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, which may not harness sound related\nproperties. The ConditionaL Neural Network (CLNN) is designed to consider the\nrelational properties across frames in a temporal signal, and its extension the\nMasked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within\nthe network, which enforces the network to learn in frequency bands rather than\nbins. Additionally, it automates the exploration of different feature\ncombinations analogous to handcrafting the optimum combination of features for\na recognition task. We applied the MCLNN to the environmental sounds of the\nESC-10 dataset. The MCLNN achieved competitive accuracies compared to\nstate-of-the-art convolutional neural networks and hand-crafted attempts.","url_abs":"http://arxiv.org/abs/1804.02665v2","url_pdf":"http://arxiv.org/pdf/1804.02665v2.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":"environmental-sound-recognition-using-masked","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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}