• Speech Sciences
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• v.10 no.4
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• pp.107-115
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• 2003

In this paper we tried to classify the pathological voice signal with severe noise component based on two different parameters, the spectral slope and the ratio of energies in the harmonic and noise components (HNR), The spectral slope is obtained by using a curve fitting method and the HNR is computed in cepstrum quefrency domain. Speech data from normal peoples and patients are collected, diagnosed and divided into three different classes (normal, relatively less noisy and severely noisy data), The mean values and the standard deviations of the spectral slope and the HNR are computed and compared with in the three kinds of data to characterize and classify the severely noisy pathological voice signals from others.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.687-696
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• 2019

Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.664-666
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• 2011

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.691-692
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• 2013

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.65-68
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• 2001

In the rehabilitation robotic arm systems for the disabled with spinal code injury, EMG signals are used in the control of the robotic arm. EMG signals are corrupted by many kinds of noises such as ECG signal, power noise and contact noise of electrode. Noise rejection improves the performance of the EMG pattern classification. In this paper, a variable bandwidth filter (VBF) and wavelet transform are used for the noise rejection of EMG signals and the comparison of SNR is given. Also, some statistical characteristics of features are investigated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.367-371
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• 2005

Social noise survey on multiple residential noises such as nut impact, air-borne, bathroom, drainage and traffic noises was conducted to investigate major variables affecting the overall satisfaction for noise environment The effect of individual noise perception on the evaluation of the overall noise environment was investigated through a questionnaire survey on annoyance, disturbance and noise sensitivity. Auditory experiments was also undertaken to determine noise level according to the percent of satisfaction for individual noise source. As a result of survey, it was found that satisfaction for floor impact noise most greatly affects the overall satisfaction for noise environment and annoyance most greatly affects the satisfaction for individual noise sources. Result of auditory experiment showed that the noise level of floor impact noise by bang machine, airborne, drainage and traffic noise corresponding to 50% satisfaction is 44dB($L_{i,Fmax,AW}$) and 40dBA, respectively.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.712-717
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• 2004

Recently, the concern for the environmental noise has increased due to the growing of the living standard. The environmental noise regulations based on the equivalent noise level are widely used. However, the noise level, which Is based mainly on the magnitude with A-weighting, the important characteristics of noises in frequency and time domains and the impulsive nature cannot be assessed properly. These can have substantial effects on how human respond to noise. Therefore, the noise evaluation methodology based on the sound quality rather than the equivalent noise level can be more suitable to represent human response to the environmental noise. This paper describes the study on environmental noise quality analysis for various noises. A cluster analysis was carried out and the noises were classified into several clusters using the values of sound quality metrics. The classification was confirmed by comparing time and frequency characteristics of the noises. And then the result of Jury testing was analysis.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.71-81
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• 1995

In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.