Noise-Robust Sound-Event Classification System with Texture Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Yongju | - |
dc.contributor.author | Atif, Othmane | - |
dc.contributor.author | Lee, Jonguk | - |
dc.contributor.author | Park, Daihee | - |
dc.contributor.author | Chung, Yongwha | - |
dc.date.accessioned | 2021-09-02T06:51:01Z | - |
dc.date.available | 2021-09-02T06:51:01Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.issn | 2073-8994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/73286 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | ALGORITHM | - |
dc.title | Noise-Robust Sound-Event Classification System with Texture Analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Jonguk | - |
dc.contributor.affiliatedAuthor | Park, Daihee | - |
dc.contributor.affiliatedAuthor | Chung, Yongwha | - |
dc.identifier.doi | 10.3390/sym10090402 | - |
dc.identifier.scopusid | 2-s2.0-85054379208 | - |
dc.identifier.wosid | 000447336700048 | - |
dc.identifier.bibliographicCitation | SYMMETRY-BASEL, v.10, no.9 | - |
dc.relation.isPartOf | SYMMETRY-BASEL | - |
dc.citation.title | SYMMETRY-BASEL | - |
dc.citation.volume | 10 | - |
dc.citation.number | 9 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordAuthor | sound-event classification | - |
dc.subject.keywordAuthor | noise robustness | - |
dc.subject.keywordAuthor | texture analysis | - |
dc.subject.keywordAuthor | dominant neighborhood structure | - |
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