• 한국소음진동공학회논문집
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• 제22권4호
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• pp.318-327
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• 2012

The operating sound radiated from a laser printer includes tonal noise components caused by the rotating mechanical parts such as gear, shaft, motor, fan, etc. The negative effects of the tonal noise components need to be considered in the process of developing a sound quality index for the quantitative evaluation of the emotional satisfaction in terms of psycho-acoustics. However, in a previous paper, it was confirmed that the Aures tonality did not have enough correlation with the results of jury evaluation. The sound quality index based on loudness, articulation index, fluctuation strength has a little problem in considering the effects of rotating mechanical parts on the sound quality. In this paper, to solve the tonality evaluation problem, the calculation algorithm of Aures tonality was investigated in detail to find the cause of decreasing the correlation. The new tonality evaluation model was proposed by modifying and optimizing the masking effect, loudness ratio, and shape of weighting curve based on the basic algorithm of Aures tonality, and applied to two kinds of operating sound groups in order to verify the usefulness of proposed model. As a result, it is confirmed that the proposed tonality evaluation model has enough correlation and usefulness for expressing the tonalness in the operating sounds of laser printers. In the following paper, this results will be used to model the sound quality index as the input data by using the classification algorithm.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.521-526
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• 2003

In this study the traffic noises were investigated for the subjective allowing limitation ion and the testified classes, 7 point scale was selected to evaluate the annoyance level with vocabularies. As a result, 'relatively annoying' is the most suitable expression for the allowing 1imitation, and the sound pressure levels for the traffic was 44.4㏈.

• 한국주거학회논문집
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• 제12권3호
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• pp.149-156
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• 2001

Recently, with the increase of income level, many people desire to have agreeable and quiet surrounding. However, construction noise has caused much annoyance for a number of dwellers and workers in nearby construction field. It has become a very serious issue in our living environment. For the accurate evaluation of construction noise with various frequency spectrums and fluctuation characteristics, the evaluation system should reflect not only physical quantities but also the psychological respects of individual persons. With preceding study of psycho-acoustical experiments, this study attempts to survey the classification of adjectives and sound using the method of selected description and intends to get the basic data for establishment of a standard about construction noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.616-621
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• 2006

A began of this study is to verify Spontaneous Acoustic Field Reproduction System(SAFRS), developed as an embodiment of creating agreeable sound environment, with evaluation on the field application. SAFRS is a system to sense changes of surroundings and produce sounds, which can go well with environment elements sensed by the system in to the space. The sound which can go well with environment elements is sound which judged by individual evaluation to be so, the classification of the preferred sounds according to the mood of the space was suggested in the former study. So, SAFRS was applied into the Square of D University to evaluate effectiveness of the system. The executed evaluations were 1) evaluation on sounds perception, frequency, volume and matchability with the space, 2) image evaluation on the square and sound environment and 3) evaluation on sound environment with existing sounds, fountains sound, sound produced by SAFRS, and both fountains sound and sound produced by SAFRS. Verifying SAPRS of field application was deduced from those evaluations. Theresultsofthestudyarefollowing: Though the system was applied into the space, the volume of the sounds shouldn't be too high. And with visual surroundings, the effectiveness of the system would be increased. At the results of four evaluations, the result of day evaluation is; both fountains sound and sound produced by SAFRS>fountains sound>sound produced by SAFRS>existing sounds, the result of night evaluation is; sound produced by SAFRS>both fountains sound and sound produced by SAFRS>fountains sound>existing sounds and these results pointed out that sounds environment produced by the system was highly evaluated due to less background sounds.

• 한국소음진동공학회논문집
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• 제11권6호
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• pp.200-207
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• 2001

In this paper, a new method for Partial field decomposition is developed that is based on the beamforming algorithm for the application of acoustical holography to a composite sound field generated by multiple incoherent sound sources. In the proposed method, source Positions are first predicted by MUSIC(multiple signal classification) algorithm. The composite sound fields can then be decomposed into each partial field by the beamforming. Results of both numerical simulations and experiments show that the method can find each partial field very accurately and effectively, and that it also has Potential to be used for application to distributed sources.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1997년도 영남지회 학술발표회 논문집 Acoustic Society of Korean Youngnam Chapter Symposium Proceedings
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• pp.68-74
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• 1997

Ship radiated noise is an infortant parameter which dtermines Anti Submarine Warfare(ASW) countermeansure or passive Sonar detection and classification performance. Its measurement should be performed under controlled ocean acoustic environment. In data reduction of the measured data from hydrophone array, theeffect fo ambient noise, surface reflection and bottom reflection etc. should be compensated to obtain the source level of the ship radiated noise. This study describes the measurement hydrophone array design criteria based on the analysis of transimission anomaly due to the surface reflection.

• 방송공학회논문지
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• 제25권6호
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• pp.854-860
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• 2020

심층 신경망은 영상 분류 그리고 음성 인식 등 다양한 분야에서 뛰어난 성능을 보여주었다. 그 중에서 데이터 증대를 통해 생성된 다양한 데이터는 신경망의 성능을 향상하게 시키는 데 중요한 역할을 했다. 일반적으로 데이터의 변형을 통한 증대는 신경망이 다채로운 예시를 접하고 더 일반적으로 학습되는 것을 가능하게 했다. 기존의 영상 분야에서는 신경망 성능 향상을 위해 새로운 증대 방법을 제시할 뿐만 아니라 데이터와 신경망의 구조에 따라 변화할 수 있는 최적의 데이터 증대 방법의 탐색 방법을 제안해왔다. 본 논문은 이에 영감을 받아 음향 분야에서 최적의 데이터 증대 방법을 탐색하는 것을 목표로 한다. 잡음 추가, 음의 높낮이 변경 혹은 재생 속도를 조절하는 등의 증대 방법들을 다양하게 조합하는 실험을 통해 경험적으로 어떤 증대 방법이 가장 효과적인지 탐색했다. 결과적으로 자연 음향 데이터 세트 (ESC-50)에 최적화된 데이터 증대 방법을 적용함으로써 분류 정확도를 향상하게 시킬 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 춘계학술대회논문집
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• pp.1243-1246
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• 2006

As remodeling market is growing and peoples' concern on health and well-being is getting high, there is a need to apply environmentally friendly approach to remodeling an apartment houses. But, in point of the impact noise concerned, the thickness of the concrete slab and the limited ceiling height of the remodelling houses are the main constraints to improve the impact noise performance. In order to investigate the effect of the impact noise isolation as structural treatments for the structural elements, heavy-weight impact noise and tapping noise were measured in an remodeling building. As a result, structural strengthening method by H-beam was successful to enhance the impact noise level at about 3 or 4 class by the sound classification system.