{"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/recognition-of-acoustic-events-using-masked","title":"Recognition of Acoustic Events Using Masked Conditional Neural Networks","arxiv_id":"1802.02617","date":"2018-02-07","proceeding":null,"authors":["Fady Medhat","David Chesmore","John Robinson"],"abstract":"Automatic feature extraction using neural networks has accomplished\nremarkable success for images, but for sound recognition, these models are\nusually modified to fit the nature of the multi-dimensional temporal\nrepresentation of the audio signal in spectrograms. This may not efficiently\nharness the time-frequency representation of the signal. The ConditionaL Neural\nNetwork (CLNN) takes into consideration the interrelation between the temporal\nframes, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN\nby forcing a systematic sparseness over the network's weights using a binary\nmask. The masking allows the network to learn about frequency bands rather than\nbins, mimicking a filterbank used in signal transformations such as MFCC.\nAdditionally, the Mask is designed to consider various combinations of\nfeatures, which automates the feature hand-crafting process. We applied the\nMCLNN for the Environmental Sound Recognition problem using the Urbansound8k,\nYorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive\nperformance compared to state-of-the-art Convolutional Neural Networks and\nhand-crafted attempts.","url_abs":"http://arxiv.org/abs/1802.02617v2","url_pdf":"http://arxiv.org/pdf/1802.02617v2.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":"recognition-of-acoustic-events-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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}