• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.707-711
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• 2004

Recently, the interests about noises have increased with the rapid development of our living environment Until now the estimation methods to sounds have used the equivalent levels. The sensitivities of human beings aren't considered in these methods. It's a situation to need new estimation methods for environmental noises. They must be analyzed by the characteristics of sounds before making the noise regulations newly. In this study, the noises were measured around our living environment And the frequency analysis, Sound Quality Metrics, the cluster analysis and so on are used to classify the environmental noises.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.722-725
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• 2006

Studies on the ways to construct agreeable sound-amenity have been processed in Korea recently and Virtual Acoustics Field Simulation System (VAFSS) which is an active acoustics reproducing system has been made as a technique to realize the results of the study. This system catches the changes of surroundings and produce sounds which go well with the mood of the space. The fact that a man thinks a sound goes well with factors of the environment should be an individual evaluation. Thus, the standards to classify factors influencing the preference of the sound, which can be judged by the environment, are needed. This study suggests the standards of factors to provide agreeable sound for people according to changes of the time and other elements. Among the factors influencing environment, the temperature, the humidity and the wind were suggested as standards of discomfort Index and wind chin temperature. Besides, only the intensity of illumination has been chosen to estimate the intensity of radiation as a part of factors of the whether.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.209-212
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• 2002

This article introduce Comfort Class V released by Dnv The Class has an objective of classifying ships by noise and vibration levees. It is apparent that noise criteria listed in the Class can not be satisfied without special efforts to enhance sound insulation performance in cabins. Accordingly, panel makers should develope panels which have very high sound insulation performance enough to satisfy the criteria. Also, due to large difference of sound insulation performance between cabin and laboratory, it is necessary that ship yards should improve the method for installing cabins.

• 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

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1606-1613
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• 2021

Recently various attempts to control hardware through integration of sensors and artificial intelligence have been made. This paper proposes a smart hazardous sound detection at home. Previous sound recognition methods have problems due to the processing of background sounds and the low recognition accuracy of high-frequency sounds. To get around these problems, a new MFCC(Mel-Frequency Cepstral Coefficient) algorithm using Wiener filter, modified filterbank is proposed. Experiments for comparing the performance of the proposed method and the original MFCC were conducted. For the classification of feature vectors extracted using the proposed MFCC, DNN(Deep Neural Network) was used. Experimental results showed the superiority of the modified MFCC in comparison to the conventional MFCC in terms of 1% higher training accuracy and 6.6% higher recognition rate.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.79-82
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• 2005

This study aims to propose fundamental data for development of noise reduction system that is applied to classification for light-weight impact sound. For this reason, eight types of damping materials were constructed in new construction field. Comparison and analysis among the reduction materials were carried out on the acoustical characteristics through test. In the end, the suitability as a damping material was evaluated by the analysis.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.6
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• pp.362-371
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• 2022

As considerable interests in noise emission from the ships have been increased, International Maritime Organization (IMO) standardized the Underwater Radiated Noise (URN) measurement process of commercial ships in deep seas by enacting the related ISO standard ISO 17208-1 and classification societies responded with the enactment or revision of corresponding notations. According to this trend, a new hybrid underwater sound propagation model based on underwater sound propagation theories was developed and its accuracy on analysis was verified through the result comparison with the results of other generally used models. Using the verified model, each URN propagation characteristics adjusted by the correction methods proposed in the ISO standard and class notations were analyzed and compared in two assumed URN measurement cases. The results showed that the effects of transmission loss corrections in the circumstances with less bottom reflections generally similar but they had rather large differences in the model analysis results with bottom-reflection-dominant conditions. It was concluded that the deep consideration of effective bottom-reflection-correction method should be made in future revisions of ISO standard and class notations.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.350-357
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• 2022

A national park is a place to conserve natural resources and visitors to experience nature, and thus, it is necessary to identify the noise distribution characteristic in the national park and preserve and restore the soundscape. However, most national parks in Korea are exposed to noise, leading to negative perceptions of the national park's soundscape and affecting the ecosystem. Many national parks in other countries have investigated the ecosystem impacts caused by noise and have performed various management to reduce the noise. However, in Korea, there is still a lack of awareness of the effect on the ecosystem, overlooking the need for soundscape management. Therefore, in this study, we developed a noise map of Mudeungsan National Park to investigate the quantitative impact of noise on visitors and the ecosystem. Also, we measured the trail's soundscape to describe a sound grade classification, and the soundscape of main spots in the park was recorded for a year and then analyzed. Finally, the sound resource distribution map was described, which can be used as preliminary data to determine the national park's sound distribution characteristics and manage the soundscape.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.81-86
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• 2014

In this paper, we present an effective sound classification system for recognizing the real-time context of a smartphone user. Our system avoids unnecessary consumption of limited computational resource by filtering both silence and white noise out of input sound data in the pre-processing step. It also improves the classification performance on low energy-level sounds by amplifying them as pre-processing. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the feature vectors through k-means clustering. We collected a large amount of 8 different type sound data from daily life in a university research building and then conducted experiments using them. Through these experiments, our system showed high classification performance.