Noise-Robust Sound-Event Classification System with Texture Analysis
- Authors
- Choi, Yongju; Atif, Othmane; Lee, Jonguk; Park, Daihee; Chung, Yongwha
- Issue Date
- 9월-2018
- Publisher
- MDPI
- Keywords
- sound-event classification; noise robustness; texture analysis; dominant neighborhood structure
- Citation
- SYMMETRY-BASEL, v.10, no.9
- Indexed
- SCIE
SCOPUS
- Journal Title
- SYMMETRY-BASEL
- Volume
- 10
- Number
- 9
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/73286
- DOI
- 10.3390/sym10090402
- ISSN
- 2073-8994
- 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.
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- Appears in
Collections - College of Science and Technology > Department of Computer Convergence Software > 1. Journal Articles
- Graduate School > Department of Computer and Information Science > 1. Journal Articles
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