• 한국음향학회지
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• 제20권2호
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• pp.86-92
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• 2001

소음원 위치와 방위를 규명하기 위해 다양한 배열처리기술이 발전되어 왔다. 배열처리기술의 기본은 두 개의 수신센서에 수신된 신호의 시간차를 이용하여 소음원의 위치와 방위를 구하는 것으로 응용분야나 신호처리방법에 따라 고유의 특성을 갖는 빔형성기법, 상관함수기법 및 NAH (Near-Field Acoustic Holography) 등이 있다 본 연구에서는 이러한 기법들 중 광대역 소음원 탐지에 적용되는 상관함수기법을 채택하여 소음원의 대역폭과 측정 잡음원 간의 상관 관계가 위치나 방위 탐지 정확도에 미치는 영향을 분석하여 효과적인 소음원 탐지기법을 제안한다. 본 연구에서 채택한 배열의 기하학적 형상은 위치나 방위의 3차원적 모호성을 없애기 위한 3차원 비선형이며 제안된 기법의 타당성은 수치모의 실험 및 실제 실험으로 검증되었다.

• Smart Structures and Systems
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• 제18권5호
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• 한국해양공학회지
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• 제22권1호
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• 소음진동
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• 제7권2호
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• pp.255-260
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• 1997

This paper describes 3-dimensional volume array of 4 microphones including a reference microphone which is capable of imaging wideband noise source position in 2-dimensional image plane. The cross correlation function and corresponding imaging function between a reference microphone and other microphone, are derived as a function of noise source position. The magnitude of the imaging function gives noise source mapping in image plane. Since the image plane is selective from a rectangular and a cylindrical plane, noise source position information such as range and bearing relative to the array is identified very much easily. Simulation results for typical source configurations confirms the applicability of the proposed array in noise control field.

• 한국군사과학기술학회지
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• 제13권4호
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• pp.528-533
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• 2010

This paper describes a calculation method of source level of a ship transient noise, which is one of the important elements for the ship detection. Aim of transient noise measurements is to evaluate of acoustic energy due to singular occurrence, which is therefore defined as non-periodic and short termed events like an attack periscope, a rudder and a torpedo door. In generally, in the case of randomly spaced impulse, the spectrum becomes a broadband random noise with no distinctive pattern. Therefore, frequency analysis is not particularly revealing for type of signal. In the paper, it is performed in time domain to analyze a transient noise. However, a source level of transient noise is required an investigation for multiple frequency band. So, in order to calculate a source level of transient noise, a design of exponential weighting function, convolution, band pass filtering, peak detection, root mean square, and parameter compensation are applied. The effectiveness of this calculation scheme is studied through computer simulations and a sea test. Furthermore, an application of the method is applied in a real case.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• KEPCO Journal on Electric Power and Energy
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• 제1권1호
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• pp.99-104
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• 2015

In this study, it has been initiated to investigate the specific abnormal vibration signal that has been captured in the power equipment. Array Microphone can be used in order to detect the direction and the position of the noise source. It is possible to track the abnormal mechanical noise in the power plant by utilizing the program and the microphone array system developed from this research. Array microphone system can be operated as a constant monitoring system.

• The Journal of the Acoustical Society of Korea
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• 제28권3E호
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• pp.79-87
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• 2009

This paper reviews several multi-source localization methods which estimate ITDs based on zero crossings (ZCs). Employing signal-to-noise ratio (SNR) estimation from ITD variances, these ZC-based source localization methods are more robust to diffuse noise than the cross-correlation (CC)-based one with less computational complexity. In order to take reverberant environments into account, two approaches detect intervals which dominantly contain direct-path components from sources to sensors because they may effectively provide reliable ITDDs corresponding to source directions. One accomplishes the detection by comparing the original and cepstral-prefiltering-processed envelopes, and the other searches sudden increase of acoustic energy by considering typical characteristics of acoustic reverberation. Experiments for comparison of these methods demonstrate that the approach with energy-based detection efficiently achieves multi-source localization in reverberant environments.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2010년도 춘계학술대회 논문집
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• pp.425-426
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• 2010

Recently, Most of diverse noise of vehicles has decreased competitively according to development of the automotive manufacturing technology. Especially, Passenger car manufacturers has been conducting buzz, squeak and rattle(BSR) noise test as a method of the noise evaluation tests to reduce an unpleasant sound from interior parts on the driving the car. This paper suggest a evaluation method for detecting position of noise source from measured noise signals of vehicle seats during random excitation BSR test. Hereby the BSR test procedure used the test regulation of 'G' company. The detection of noise source positions used the Sound image equipment. Through suggested the test method on this paper, an accurate analysis of noise source occurred in the BSR test will be possible.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.