{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:57:28Z","timestamp":1761490648714,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,15]],"date-time":"2018-09-15T00:00:00Z","timestamp":1536969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Sound-event classification has emerged as an important field of research in recent years. In particular, investigations using sound data are being conducted in various industrial fields. However, sound-event classification tasks have become more difficult and challenging with the increase in noise levels. In this study, we propose a noise-robust system for the classification of sound data. In this method, we first convert one-dimensional sound signals into two-dimensional gray-level images using normalization, and then extract the texture images by means of the dominant neighborhood structure (DNS) technique. Finally, we experimentally validate the noise-robust approach by using four classifiers (convolutional neural network (CNN), support vector machine (SVM), k-nearest neighbors(k-NN), and C4.5). The experimental results showed superior classification performance in noisy conditions compared with other methods. The F1 score exceeds 98.80% in railway data, and 96.57% in livestock data. Besides, the proposed method can be implemented in a cost-efficient manner (for instance, use of a low-cost microphone) while maintaining high level of accuracy in noisy environments. This approach can be used either as a standalone solution or as a supplement to the known methods to obtain a more accurate solution.<\/jats:p>","DOI":"10.3390\/sym10090402","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T10:42:20Z","timestamp":1537180940000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Noise-Robust Sound-Event Classification System with Texture Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4661-6196","authenticated-orcid":false,"given":"Yongju","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Othmane","family":"Atif","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-4850","authenticated-orcid":false,"given":"Jonguk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.neucom.2017.07.021","article-title":"Noise Robust Sound Event Classification with Convolutional Neural Network","volume":"272","author":"Ozer","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ins.2017.02.013","article-title":"Robust Acoustic Event Classification Using Deep Neural Networks","volume":"396","author":"Sharan","year":"2017","journal-title":"Inf. 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