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http://dx.doi.org/10.5909/JBE.2020.25.6.845

Environmental Sound Classification for Selective Noise Cancellation in Industrial Sites  

Choi, Hyunkook (Dept. of Electronics Engineering, Kwangwoon University)
Kim, Sangmin (Dept. of Electronics Engineering, Kwangwoon University)
Park, Hochong (Dept. of Electronics Engineering, Kwangwoon University)
Publication Information
Journal of Broadcast Engineering / v.25, no.6, 2020 , pp. 845-853 More about this Journal
Abstract
In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.
Keywords
industrial noise; sound classification; deep neural network; industrial site;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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