• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.872-876
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• 2007

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• 한국소음진동공학회논문집
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• 제22권2호
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• pp.193-197
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• 2012

Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.153-158
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• 2012

Noise maps are produced according to 'The Method of making a Noise Map' in order to noise control efficiently, and prediction model to predict road traffic noise which may apply to Korean situation, include CRTN, RLS 90, NMPB, Nord 2000 and ASJ 2003. Of them, ASJ 2003, Japan's prediction model has not been verified for the application to Korean situation according to the classification of vehicle. In addition, ASJ 2003 was revised to ASJ 2008 recently, a classification for motorcycle was added. This study attempts to check the classification of vehicle in ASJ 2008 and 'The Method of making a Noise Map' to confirm the suitability of the application of them to Korean situation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.486-491
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• 2002

Auditory experiments based on subjective responses were undertaken for the standard heavy and light weight impact noise. Relations between noise levels and subjective evaluations were also investigated. As a result, it was shown that the noise class was rated with the range of sensible satisfaction by investigating the various social responses for the floor impact noise. The rate classification for the heavy weight impact noise is suggested as a design guide for concrete slabs which satisfy the residents' requirements in various sound insulation capacities of multistory residential buildings.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.636-640
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• 2006

In this study, metrics for evaluation of heavy-weight impact noise were investigated. Heavy-weight impact noises generated by impact ball were recorded in real apartments using binaural microphone. Those sounds were classified into three groups according to frequency characteristics in order to control aspects which affect subjective responses to heavy-weight impact noise. Sound sources for auditory experiment were selected based on the classification result. Then auditory experiments were conducted to investigate the relationship between level indices and subjective responses. The results showed that $L_{Aeq},\;L_{Amax}$ and $LL_z$ as well as $L_{iFmax,AW}$ were highly correlated with subjective response. Therefore, $L_{Aeq}$ and $L_{Amax}$ can be used as metrics for evaluation of heavy-weight impact noise. In further studies, it is needed to verify classification of heavy-weight impact noise generated by bang machine and impact ball.

• 방송공학회논문지
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• 제25권6호
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• pp.845-853
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• 2020

본 논문에서는 산업현장에서의 선택적 소음 제거를 위한 환경 사운드 분류 기술을 제안한다. 산업현장에서의 소음은 작업자의 청력 손실의 주요 원인이 되며, 소음 문제를 해결하기 위한 소음 제거 기술이 널리 연구되고 있다. 그러나 기존 소음 제거 기술은 모든 소리를 구분 없이 차단하는 문제를 가지며, 모든 소음에 공통된 제거 방법을 적용하여 각 소음에 최적화된 소음 제거 성능을 보장할 수 없다. 이러한 문제를 해결하기 위해 사운드 종류에 따라 선택적 동작을 하는 소음 제거가 필요하고, 본 논문에서는 이를 위해 딥 러닝 기반의 환경 사운드 분류 기술을 제안한다. 제안 방법은 기존 오디오 특성인 멜-스펙트로그램의 한계를 극복하기 위해 새로운 특성으로서 멜-스펙트로그램 기반의 시간 변화 특성과 통계적 주파수 특성을 사용하며, 합성곱 신경망을 이용하여 특성을 모델링 한다. 제안하는 분류기를 사용하여 3가지 소음과 2가지 비소음으로 구성된 총 5가지 클래스로 사운드를 분류하였고, 제안하는 오디오 특성을 사용하여 기존 멜-스펙트로그램 특성을 사용할 때에 비하여 분류 정확도가 6.6% 포인트 향상되는 것을 확인하였다.

• Journal of the Korean Data Analysis Society
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• 제20권6호
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• pp.2853-2864
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• 2018

판별분석(discriminant analysis)은 새로운 개체가 입력되었을 때, 그 개체가 어느 그룹에 속하는지 예측하는데 사용되는 분석방법이다. 판별분석에서는 레이블(label)을 통해 새로운 개체를 예측하기 때문에 판별분석에서 레이블은 중요하다. 레이블 노이즈(label noise)는 관측된 레이블에 오류가 포함된 것을 의미하며, 실데이터에 발생하기 쉽고 판별성능에 영향을 미칠 수 있는 중요한 요인이다. 이를 개선하기 위해 레이블 노이즈와 레이블 노이즈에 강건한 모형들이 연구되고 있지만, 레이블 노이즈가 존재할 때 판별성능에 영향을 줄 수 있는 요인을 고려하고 이 요인들이 판별성능에 미치는 영향을 비교한 연구는 찾기 힘들다. 따라서 이 논문에서는 분류문제에서 많이 사용되는 LDA, QDA, KNN, SVM 방법을 이용하여 레이블 노이즈가 판별성능에 미치는 영향을 알아보고자 한다. 특히 판별분석의 성능과 연관이 있을 것으로 예상되는 레이블 노이즈의 발생 비율, 발생형태, 데이터의 개수에 따른 판별성능을 모의실험을 통해 살펴보았다. 그 결과, 데이터의 형태와 분석기법에 따라 레이블 노이즈가 판별성능에 영향을 미치는 정도가 다름을 확인하였다.

• 한국해양공학회지
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• 제26권4호
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.1142-1146
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• 2007

In this studies, subjective evaluation of heavy-weight floor impact sound through classification was conducted. Heavyweight impact sounds generated by an impact ball were recorded through dummy heads in apartment buildings. The recordings were classified according to the frequency characteristics of the floor impact sounds which are influenced by the floor structure with different boundary conditions and composite materials. The characteristics of the floor impact noise were investigated by paired comparison tests and semantic differential tests. Sound sources for auditory experiment were selected based on the actual noise levels with perceptual level differences. The results showed that roughness and fluctuation strength as well as loudness of the heavy-weight impact noise had a major effect on annoyance.

• 대한원격탐사학회지
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• 제25권3호
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• pp.215-223
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• 2009

The noise removal as a preprocessing was tried with various kinds of wavelet pairs. Wavelet transform for 2D images generally uses the same wavelets as basis functions in horizontal and vertical directions. A method with different wavelets was tried for each direction separately, which gives more precise interpretation of the classification. Total 486 pairs of wavelets from nine basis functions were tried to remove image noises. The classification accuracies before and after the noise removal were compared. Although all kinds of wavelet pairs showed the increased accuracies in classification, there were best and worst wavelet pairs depending on the data sets. Wavelet pairs with low energy percentage of LL band showed the high classification accuracy. A pattern was found in the results that very similar vertical accuracy was distributed for each horizontal ones. Since Haar is the shortest length filter, Haar could be a predictor wavelet to find the good wavelet pairs.