• Title/Summary/Keyword: Noise source Detection

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Sound Source Detection Technique Considering the Effects of Source Bandwidth and Measurement Noise Correlation (소음원 대역폭과 측정잡음의 상관관계를 고려한 소음원 탐지기법)

  • 윤종락
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.2
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    • pp.86-92
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    • 2001
  • Various array processing techniques to identify the noise source position or bearing have been developed. Typical array processing techniques which are based on time delay between received signals at two sensors, are classified as conventional beamforming, correlation function and NAH (Near-Field Acoustic Holography) techniques which have their own characteristics with respect to application field and signal processing method. In this study, correlation function technique which could be applied for broadband noise source detection, is adopted and the effective detection technique is proposed considering the effects of source bandwidth and measurement noise correlation of noise sources. The validity of the Proposed technique is evaluated using the 3-dimensional nonlinear any which does not give 3-dimensional Position or bearing ambiguity

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Acoustic emission source location and noise cancellation for crack detection in rail head

  • Kuanga, K.S.C.;Li, D.;Koh, C.G.
    • Smart Structures and Systems
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    • v.18 no.5
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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.

Identification of Underwater Ambient Noise Sources Using Hilbert-Huang Transfer (힐버트-후앙 변환을 이용한 수중소음원의 식별)

  • Hwang, Do-Jin;Kim, Jea-Soo
    • Journal of Ocean Engineering and Technology
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    • v.22 no.1
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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.

Microphone Array Design for Noise Source Imaging (소음원 영상화를 위한 마이크로폰 배열 설계)

  • ;Glegg, Stewart A.L.
    • Journal of KSNVE
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    • v.7 no.2
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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.

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A Calculation Method of Source Level of Underwater Transient Noise by Frequency Band (주파수 대역별 수중 순간소음 음원준위 산출 기법)

  • Choi, Jae-Yong;Oh, Jun-Seok;Lee, Phil-Ho
    • Journal of the Korea Institute of Military Science and Technology
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    • v.13 no.4
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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.

Real-time automated detection of construction noise sources based on convolutional neural networks

  • Jung, Seunghoon;Kang, Hyuna;Hong, Juwon;Hong, Taehoon;Lee, Minhyun;Kim, Jimin
    • International conference on construction engineering and project management
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    • 2020.12a
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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.

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Development of Noise Source Detection System using Array Microphone in Power Plant Equipment (배열형 음향센서를 이용한 발전설비 소음원 탐지시스템 개발)

  • Sohn, Seok-Man;Kim, Dong-Hwan;Lee, Wook-Ryun;Koo, Jae-Raeyang;Hong, Jin-Pyo
    • KEPCO Journal on Electric Power and Energy
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    • v.1 no.1
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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.

Comparison of Sound Source Localization Methods Based on Zero Crossings

  • Park, Yong-Jin;Lee, Soo-Yeon;Park, Hyung-Min
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.3E
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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.

Experimental Evaluation Method for Investigating BSR Noise of Vehicle Seats (차량용 시트의 BSR Noise 규명을 위한 시험적 평가방법)

  • Kim, Byung-Jin;Moon, Nam-Su;Park, Jin-Sung;Park, Hyun-Woo
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2010.05a
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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.

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CNN based Sound Event Detection Method using NMF Preprocessing in Background Noise Environment

  • Jang, Bumsuk;Lee, Sang-Hyun
    • International journal of advanced smart convergence
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    • v.9 no.2
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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.