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Статті в журналах з теми "Acoustic source identification"

Автор: Grafiati

Опубліковано: 23 лютого 2024

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1

Deng, Jiang Hua, Jun Hong Dong, and Guang De Meng. "Sound Source Identification and Acoustic Contribution Analysis Using Nearfield Acoustic Holography." Advanced Materials Research 945-949 (June 2014): 717–24. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.717.

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Анотація:

The main goal of the present paper is to provide a method of source identification. Firstly, statistically optimal near-field acoustical holography (SONAH) techniques are applied to locate sound sources with the reflected sound field. In the presence of reflection plane parallel and perpendicular to the source plane, the incoming wave and reflected waves are separated based on the acoustic superposition principle and acoustic mirror image principle to satisfy the condition of the sound sources reconstruction using SONAH. Secondly, contribution of noise source to the special field point is analyzed and noise source ranking of interior panel groups are evaluated based the proposed three step acoustic contribution method. Finally, this method is verified experimentally.

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2

Wróbel, Jakub, and Damian Pietrusiak. "Noise Source Identification in Training Facilities and Gyms." Applied Sciences 12, no. 1 (December 22, 2021): 54. http://dx.doi.org/10.3390/app12010054.

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This paper deals with noise problems in industrial sites adapted for commercial training venues. The room acoustics of such an object were analyzed in the scope of the reverberation time and potential acoustic adaptation measures are indicated. Identification and classification of noise sources in training facilities and gyms was carried out based on the acoustic measurements. The influence of rubber padding on impact and noise reduction was investigated in the case of chosen noise-intensive exercise activities performed in a previously described acoustic environment. Potential noise reduction measures are proposed in the form of excitation reduction, vibration isolation, and room acoustics adaptation.

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3

Samet, A., M. A. Ben Souf, O. Bareille, M. N. Ichchou, T. Fakhfakh, and M. Haddar. "Structural Source Identification from Acoustic Measurements Using an Energetic Approach." Journal of Mechanics 34, no. 4 (May 15, 2017): 431–41. http://dx.doi.org/10.1017/jmech.2017.24.

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AbstractAn inverse energy method for the identification of the structural force in high frequency ranges from radiated noise measurements is presented in this paper. The radiation of acoustic energy of the structure coupled to an acoustic cavity is treated using an energetic method called the simplified energy method. The main novelty of this paper consists in using the same energy method to solve inverse structural problem. It consists of localization and quantification of the vibration source through the knowledge of acoustic energy density. Numerical test cases with different measurement points are used for validation purpose. The numerical results show that the proposed method has an excellent performance in detecting the structural force with a few acoustical measurements.

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4

Ping, Guoli, Zhigang Chu, and Yang Yang. "Compressive Spherical Beamforming for Acoustic Source Identification." Acta Acustica united with Acustica 105, no. 6 (November 1, 2019): 1000–1014. http://dx.doi.org/10.3813/aaa.919406.

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This study examines a compressive spherical beamforming (CSB) method, using a rigid spherical microphone array to localize and quantify the acoustic contribution of sources. The method relies on the array signal model in the spherical harmonics domain that can be represented as a spatially sparse problem. This makes it possible to use compressive sensing to solve an underdetermined problem via promoting sparsity. The estimation of the angular position of sources with respect to the microphone array, as well as the three-dimensional localization over a volume are investigated. Several sparse recovery algorithms [orthogonal matching pursuit (OMP), generalized OMP, ϱ1-norm minimization, and reweighted ϱ1-norm minimization] are examined for this purpose. The numerical and experimental results indicate that sparse recovery methods outperform conventional spherical harmonics beamforming. Reweighted ϱ1-norm has good adaptability to noise, improving the robustness of CSB. The method can successfully localize the angular position of sources, and quantify their relative pressure contribution. The method is promising to localize sources in a three-dimensional domain of interest. However, the three-dimensional localization is more sensitive to noise, source distance, and properties of the sensing matrix than the two-dimensional localization.

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5

Dou, Yan Tao, Xiao Li Xu, Xiao Jun Cai, Guo Xin Wu, and Zhi Xiang Sun. "The AE Identification Methods to Welding Defect by Wavelet Analysis." Applied Mechanics and Materials 63-64 (June 2011): 355–60. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.355.

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In the engineering applications area, welding defect is a major hidden danger of structure security. the bending failure process of welded specimens is detected by using AE technique and the data samples of typical welding defect source are collected, and by using wavelet technique the typical AE datas acquired through experiment are analyzed, characteristic information of the typical acoustic emission source such as electromagnetic noise, plastic deformation, micro-crack initiation, crack unsteady expansion and fracture, etc are extracted. A serial acoustic emission source identification methods based on the energy spectrum coefficients of wavelet are established and which can realize accurately distinguishing of different acoustic emission sources, so as to provide a theoretical basis to detect equipment welding defects by acoustic emission technology dynamic in engineering practice.

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6

Eller, Matthias, and Nicolas P. Valdivia. "Acoustic source identification using multiple frequency information." Inverse Problems 25, no. 11 (October 5, 2009): 115005. http://dx.doi.org/10.1088/0266-5611/25/11/115005.

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7

Friesel, M. A. "Acoustic emission source identification using longwaveguide sensors." NDT International 19, no. 3 (June 1986): 203–6. http://dx.doi.org/10.1016/0308-9126(86)90110-0.

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8

Ping, Guoli, Zhigang Chu, Yang Yang, and Xu Chen. "Iteratively Reweighted Spherical Equivalent Source Method for Acoustic Source Identification." IEEE Access 7 (2019): 51513–21. http://dx.doi.org/10.1109/access.2019.2911857.

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9

Prateepasen, Asa, Chalermkiat Jirarungsatean, and Pongsak Tuengsook. "Identification of AE Source in Corrosion Process." Key Engineering Materials 321-323 (October 2006): 545–48. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.545.

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In this paper acoustic emission (AE) was implemented to detect and study the corrosion on austenitic stainless steel grade AISI 304. Two tests were conducted at room temperature using an acidic 30% Chloride solution in passive tests procedure and 3% NaCl solution in electrochemical process. From the experimental works, it appeared that AE signals could be detected during corrosion. Data were studied in time and frequency domain to characterize and to find out the relation between AE parameter and corrosion. In addition the source of generated acoustic signals and corrosive mechanism in the different corrosive environment condition were discussed.

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10

Wang, Rujia, and Shaoyi Bei. "Optimization of Fixed Microphone Array in High Speed Train Noises Identification Based on Far-Field Acoustic Holography." Advances in Acoustics and Vibration 2017 (February 1, 2017): 1–11. http://dx.doi.org/10.1155/2017/1894918.

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Acoustical holography has been widely applied for noise sources location and sound field measurement. Performance of the microphones array directly determines the sound source recognition method. Therefore, research is very important to the performance of the microphone array, its array of applications, selection, and how to design instructive. In this paper, based on acoustic holography moving sound source identification theory, the optimization method is applied in design of the microphone array, we select the main side lobe ratio and the main lobe area as the optimization objective function and then put the optimization method use in the sound source identification based on holography, and finally we designed this paper to optimize microphone array and compare the original array of equally spaced array with optimization results; by analyzing the optimization results and objectives, we get that the array can be achieved which is optimized not only to reduce the microphone but also to change objective function results, while improving the far-field acoustic holography resolving effect. Validation experiments have showed that the optimization method is suitable for high speed trains sound source identification microphone array optimization.

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11

Jablonská, Jana, Milada Kozubková, Miroslav Mahdal, Radek Štramberský, Tomáš Blejchař, and Marian Bojko. "Identification of Cavitation by Noise." MATEC Web of Conferences 369 (2022): 02010. http://dx.doi.org/10.1051/matecconf/202236902010.

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The identification of cavitation is very important in technical practice for operational and especially economic reasons. The article deals with the use of another way to measure noise during cavitation. The current approach of measuring noise with an intensity probe is used in practice for identification, but it does not immediately address the position of the cavitation source for a given frequency range. Measurement by an acoustic camera is not entirely common in practice, but it allows to determine the location of the noise source for a given frequency range. To test the acoustic camera, the authors focused on the cavitating flow in a hydraulic circuit with three previously tested nozzles. Noise was measured for these nozzles using an acoustic intensity probe with two microphones. The results were evaluated by statistical methods and compared with measurements using an acoustic camera. The aim of the article is to point out the advantages of using this approach for accurate area identification of the problem. Research background: The work is focused on the issue of cavitation and its identification in the hydraulic circuit. For cavitation research, a variant of cavitation identification by noise was chosen. However, this measurement brings problems that are only revealed through more sophisticated and accurate measurements. Purpose of the article: The purpose of the article is to point out other possibilities of measuring cavitation noise using modern technologies and subsequently verify the results. Methods: Metody: A common way of measuring noise is to measure it with a suitably located acoustic intensity probe. A more modern approach is area noise measurement. Measurement methodology and benefits are described. Findings & Value added: The commonly used way of measuring noise using an acoustic intensity probe has proved to be insufficient, as it is not possible to distinguish the location of sources in the case of complex measurements. When using an acoustic camera, there are more sources of noise in a given circuit and they are detected according to the required frequencies in different places than expected. The article points out the specific identification of noise sources using the frequency spectrum of noise for selected elements.

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12

Iwatsubo, Takuzo, Shozo Kawamura, and Masahito Kamada. "Identification of Acoustic-Vibratory System by Acoustic Measurement." Shock and Vibration 3, no. 1 (1996): 27–37. http://dx.doi.org/10.1155/1996/925970.

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A new method for reducing ill-conditioning in a class of identification problems is proposed. The key point of the method is that the identified vibration of the sound source is expressed as a superposition of vibration modes. The mathematical property of the coefficient matrix, the practical error expanding ratio, and the stochastic error expanding ratio are investigated in a numerical example. The mode-superposition method is shown to be an effective tool for acoustic-vibratory inverse analysis.

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13

Nakano, Mitsuo, Kohei Suzuki, Masao Nagamatsu, and Takuya Yoshimura. "Noise Source Identification Using Acoustic Double Holography Method." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 563 (1993): 2107–11. http://dx.doi.org/10.1299/kikaic.59.2107.

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14

Simard, Patrice, та Jerome Antoni. "Acoustic source identification: Experimenting the ℓ1 minimization approach". Applied Acoustics 74, № 7 (липень 2013): 974–86. http://dx.doi.org/10.1016/j.apacoust.2013.01.012.

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15

Yang, Yongxin, Yang Yang, and Zhigang Chu. "Off-grid deconvolution beamforming for acoustic source identification." Applied Acoustics 218 (March 2024): 109909. http://dx.doi.org/10.1016/j.apacoust.2024.109909.

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16

Zhao, Yang, Zhigang Chu, and Linyong Li. "Performance Enhancement of Functional Delay and Sum Beamforming for Spherical Microphone Arrays." Electronics 11, no. 7 (April 2, 2022): 1132. http://dx.doi.org/10.3390/electronics11071132.

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Анотація:

Functional delay and sum (FDAS) beamforming for spherical microphone arrays can achieve 360° panoramic acoustic source identification, thus having broad application prospects for identifying interior noise sources. However, its acoustic imaging suffers from severe sidelobe contamination under a low signal-to-noise ratio (SNR), which deteriorates the sound source identification performance. In order to overcome this issue, the cross-spectral matrix (CSM) of the measured sound pressure signal is reconstructed with diagonal reconstruction (DRec), robust principal component analysis (RPCA), and probabilistic factor analysis (PFA). Correspondingly, three enhanced FDAS methods, namely EFDAS-DRec, EFDAS-RPCA, and EFDAS-PFA, are established. Simulations show that the three methods can significantly enhance the sound source identification performance of FDAS under low SNRs. Compared with FDAS at SNR = 0 dB and the number of snapshots = 1000, the average maximum sidelobe levels of EFDAS-DRec, EFDAS-RPCA, and EFDAS-PFA are reduced by 6.4 dB, 21.6 dB, and 53.1 dB, respectively, and the mainlobes of sound sources are shrunk by 43.5%, 69.0%, and 80.0%, respectively. Moreover, when the number of snapshots is sufficient, the three EFDAS methods can improve both the quantification accuracy and the weak source localization capability. Among the three EFDAS methods, EFDAS-DRec has the highest quantification accuracy, and EFDAS-PFA has the best localization ability for weak sources. The effectiveness of the established methods and the correctness of the simulation conclusions are verified by the acoustic source identification experiment in an ordinary room, and the findings provide a more advanced test and analysis tool for noise source identification in low-SNR cabin environments.

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17

Gu, Can Song, Jun Hong Dong, and Hui Gao. "Apply Two Methods to Measure Engine Noise Source Identification." Applied Mechanics and Materials 536-537 (April 2014): 259–63. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.259.

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Based on sound intensity and acoustic array methods, noise source identification of an engine is measured at idle condition. The main noise source of intake surface and exhaust surface are determined, also their frequency spectrum analysis. This two kinds of method testing results fundamentally agreed very well, and verify acoustic array method is more simple and easier than sound intensity method.

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18

Nowak, Jonathan, Reinhard Wehr, Manfred Haider, and Manfred Kaltenbacher. "Inverse scheme for sound source identification in a vehicle trailer." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 6 (February 1, 2023): 1365–73. http://dx.doi.org/10.3397/in_2022_0185.

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Tire/road noise is a highly relevant topic for improving the comfort and experience of drivers and residents living in high-traffic areas. With the growing numbers of electric cars, the relevance of tire noise even increases since it is the dominant sound source in the middle-speed range. We investigate acoustic sources relevant to tire/road interactions. In doing so, we apply different sound source localization algorithms to measurement data acquired inside a large measurement trailer equipped with microphone arrays. The methods for sound source identification used are well-known beamforming-based algorithms and inverse schemes based on finite element or boundary element simulations. The latter schemes require the identification of the acoustic properties of the trailer in the stationary case. In this contribution, we present the characterization process and the results of the sound source localization in this stationary case.

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19

Bai, M. R., and J. Lee. "Industrial Noise Source Identification by Using an Acoustic Beamforming System." Journal of Vibration and Acoustics 120, no. 2 (April 1, 1998): 426–33. http://dx.doi.org/10.1115/1.2893847.

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A noise source identification technique is proposed for industrial applications by using a microphone array and beamforming algorithms. Both of the directions and the distances of long-range noise sources are calculated. The conventional method, the minimum variance (MV) method, and the multiple signal classification (MUSIC) method are the main beamforming algorithms employed in this study. The results of numerical simulations and field tests indicate the effectiveness of the acoustic beam-former in identifying noise sources in industrial environments.

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20

Ma, Ping, Fue-Sang Lien, and Eugene Yee. "Computational Acoustic Beamforming for Noise Source Identification for Small Wind Turbines." International Scholarly Research Notices 2017 (March 9, 2017): 1–24. http://dx.doi.org/10.1155/2017/7061391.

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This paper develops a computational acoustic beamforming (CAB) methodology for identification of sources of small wind turbine noise. This methodology is validated using the case of the NACA 0012 airfoil trailing edge noise. For this validation case, the predicted acoustic maps were in excellent conformance with the results of the measurements obtained from the acoustic beamforming experiment. Following this validation study, the CAB methodology was applied to the identification of noise sources generated by a commercial small wind turbine. The simulated acoustic maps revealed that the blade tower interaction and the wind turbine nacelle were the two primary mechanisms for sound generation for this small wind turbine at frequencies between 100 and 630 Hz.

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21

Xiao-Xia Guo, Xiao-Xia Guo, Rui-Qi Zhang Xiao-Xia Guo, Shu-Hao Liu Rui-Qi Zhang, Chen Wan Shu-Hao Liu, Zhen-Yu Wang Chen Wan, and Rong-Rong Han Zhen-Yu Wang. "Visualization of Rotating Machinery Noise Based on Near Field Acoustic Holography." 電腦學刊 33, no. 4 (August 2022): 215–23. http://dx.doi.org/10.53106/199115992022083304018.

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<p>In order to solve the problem of fast identification of the noise source of rotating machinery, the time-space complex envelope model of monopole sound source is studied, and a modulation method of the complex envelope is proposed. A method combining near-field acoustic holography technology and complex envelope information is proposed to reconstruct the sound field and realize the identification of rotating machinery noise sources. Using the overall fluctuation of the signal to identify the noise source of the rotating machinery greatly reduces the amount of calculation, and speeds up the positioning speed while ensuring the positioning accuracy. According to the sound field radiation characteristics of rotating machinery noise, different measurement distances, different sampling points numbers and different reconstruction distances are selected to reconstruct the sound field. The simulation data analysis results show that the near-field acoustic holography technology can still obtain high sound field reconstruction accuracy under the condition of large reconstruction distance, and does not require high sampling points numbers. Using the envelope information extracted by envelope modulation technology to reconstruct the sound field can accurately identify the number and geometric distribution of sound sources. This technology not only speeds up data processing, but also ensures the accuracy of sound field reconstruction.</p> <p> </p>

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22

Zan, Ming, Zhongming Xu, Linsen Huang, and Zhifei Zhang. "A Sound Source Identification Algorithm Based on Bayesian Compressive Sensing and Equivalent Source Method." Sensors 20, no. 3 (February 6, 2020): 865. http://dx.doi.org/10.3390/s20030865.

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Near-field acoustic holography (NAH) based on equivalent source method (ESM) is an effective method for identifying sound sources. Conventional ESM focuses on relatively low frequencies and cannot provide a satisfactory solution at high frequencies. So its improved method called wideband acoustic holography (WBH) has been proposed, which has high reconstruction accuracy at medium-to-high frequencies. However, it is less accurate for coherent sound sources at low frequencies. To improve the reconstruction accuracy of conventional ESM and WBH, a sound source identification algorithm based on Bayesian compressive sensing (BCS) and ESM is proposed. This method uses a hierarchical Laplace sparse prior probability distribution, and adaptively adjusts the regularization parameter, so that the energy is concentrated near the correct equivalent source. Referring to the function beamforming idea, the original algorithm with order v can improve its dynamic range, and then more accurate position information is obtained. Based on the simulation of irregular microphone array, comparisons with conventional ESM and WBH show that the proposed method is more accurate, suitable for a wider range of frequencies, and has better reconstruction performance for coherent sources. By increasing the order v, the coherent sources can be located accurately. Finally, the stability and reliability of the proposed method are verified by experiments.

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23

Zhou, Hualiang, Lu Lu, Mingwei Shen, Zhantao Su, and Yuxuan Huang. "An Efficient Noise Reduction Method for Power Transformer Voiceprint Detection Based on Poly-Phase Filtering and Complex Variational Modal Decomposition." Electronics 13, no. 2 (January 12, 2024): 338. http://dx.doi.org/10.3390/electronics13020338.

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The transformer is a core component in power systems, and its reliable operation is crucial for the safety and stability of the power grid. Transformer faults can be diagnosed early using acoustic signals. However, effective acoustic features are often affected by complex environmental noise, which reduces the accuracy of fault identification. As a solution, this study proposes a poly-phase filtering (PF)-based noise reduction algorithm for complex variational mode decomposition (CVMD) of multiple acoustic sources in power transformers. The algorithm dissects the received signal from the power transformer into subbands, downsizing their sampling rates via PF. Subsequently, it independently targets noise reduction within these subbands, focusing on specific acoustic sources. Leveraging complex signal transformations, we extend the variational mode decomposition (VMD) to mitigate the field of complex signals and utilize the CVMD to reduce the noise of each acoustic source within each subband for every acoustic source. The experimental results reveal that the proposed method effectively separates and denoises the sound signal of transformer operation under the interference of multiple sound sources in the substation. Its powerful noise reduction ability, combined with minimal computational complexity, greatly improves the accuracy of transformer fault identification and the reliability of the system.

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24

Zhang, Cuiqing, and Lizhen Wei. "Experiment on Noise Source Identification Based on Acoustic Array." IOP Conference Series: Earth and Environmental Science 208 (December 20, 2018): 012071. http://dx.doi.org/10.1088/1755-1315/208/1/012071.

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25

Lutfi, Robert A. "Perturbation analysis of acoustic cues for sound source identification." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2326. http://dx.doi.org/10.1121/1.4780814.

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26

Vanherzeele, Joris, Steve Vanlanduit, and Patrick Guillaume. "Acoustic source identification using a scanning laser Doppler vibrometer." Optics and Lasers in Engineering 45, no. 6 (June 2007): 742–49. http://dx.doi.org/10.1016/j.optlaseng.2006.10.008.

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27

Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.

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Анотація:

Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.

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28

Perrachione, Tyler K., Cara E. Stepp, Robert E. Hillman, and Patrick C. M. Wong. "Talker Identification Across Source Mechanisms: Experiments With Laryngeal and Electrolarynx Speech." Journal of Speech, Language, and Hearing Research 57, no. 5 (October 2014): 1651–65. http://dx.doi.org/10.1044/2014_jslhr-s-13-0161.

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Purpose The purpose of this study was to determine listeners' ability to learn talker identity from speech produced with an electrolarynx, explore source and filter differentiation in talker identification, and describe acoustic-phonetic changes associated with electrolarynx use. Method Healthy adult control listeners learned to identify talkers from speech recordings produced using talkers' normal laryngeal vocal source or an electrolarynx. Listeners' abilities to identify talkers from the trained vocal source (Experiment 1) and generalize this knowledge to the untrained source (Experiment 2) were assessed. Acoustic-phonetic measurements of spectral differences between source mechanisms were performed. Additional listeners attempted to match recordings from different source mechanisms to a single talker (Experiment 3). Results Listeners successfully learned talker identity from electrolarynx speech but less accurately than from laryngeal speech. Listeners were unable to generalize talker identity to the untrained source mechanism. Electrolarynx use resulted in vowels with higher F1 frequencies compared with laryngeal speech. Listeners matched recordings from different sources to a single talker better than chance. Conclusions Electrolarynx speech, although lacking individual differences in voice quality, nevertheless conveys sufficient indexical information related to the vocal filter and articulation for listeners to identify individual talkers. Psychologically, perception of talker identity arises from a “gestalt” of the vocal source and filter.

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29

Gardner, B. K., and R. J. Bernhard. "A Noise Source Identification Technique Using an Inverse Helmholtz Integral Equation Method." Journal of Vibration and Acoustics 110, no. 1 (January 1, 1988): 84–90. http://dx.doi.org/10.1115/1.3269485.

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A technique is developed which utilizes numerical models and field pressure information to characterize acoustic fields and identify acoustic sources. The numerical models are based on boundary element numerical procedures. Either pressure, velocity, or passive boundary conditions, in the form of impedance boundary conditions, may be imposed on the numerical model. Alternatively, if no boundary information is known, a boundary condition can be left unspecified. Field pressure data may be specified to overdetermine the numerical problem. The problem is solved numerically for the complete sound field from which the acoustic sources may be determined. The model can then be used to identify acoustic intensity paths in the field. The solution can be modified and the model used to evaluate design alternatives. In this investigation the method is tested analytical and verified. In addition, the sensitivity of the method to random and bias error in the input data is demonstrated.

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30

Uemura, Satoshi, Osamu Sugiyama, Ryosuke Kojima, and Kazuhiro Nakadai. "Outdoor Acoustic Event Identification using Sound Source Separation and Deep Learning with a Quadrotor-Embedded Microphone Array." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 329–30. http://dx.doi.org/10.1299/jsmeicam.2015.6.329.

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31

Kloser, R. J., T. Ryan, P. Sakov, A. Williams, and J. A. Koslow. "Species identification in deep water using multiple acoustic frequencies." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 6 (June 1, 2002): 1065–77. http://dx.doi.org/10.1139/f02-076.

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Multifrequency 12, 38, and 120 kHz acoustics were used to identify the dominant fish groups around a deepwater (>600 m) seamount (a known spawning site for orange roughy, Hoplostethus atlanticus) by amplitude mixing of the frequencies. This method showed three distinct acoustic groupings that corresponded to three groups of fishes based on size and swimbladder type: myctophids of total length less than 10 cm, morids and macrourids with lengths >30 cm, and orange roughy with a mean standard length of 36 cm. These three groups were the dominant groups caught in the demersal and pelagic trawls in the study area. A simple model of swimbladder resonance at depth of large and small gas-filled bladder fish groups is in agreement with our experimental observations. Traditionally, demersal and pelagic trawling is used to identify fish species in acoustic records. However, orange roughy are rarely caught in mid-water owing to net avoidance. Using three frequencies, these groups could be distinguished directly over their entire vertical extent from the acoustic records. This reduces a major source of positive bias uncertainty (factor range of 2.06.4) in the orange roughy biomass estimates.

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32

Davis, Ian, and Gareth J. Bennett. "Novel Noise-Source-Identification Technique Combining Acoustic Modal Analysis and a Coherence-Based Noise-Source-Identification Method." AIAA Journal 53, no. 10 (October 2015): 3088–101. http://dx.doi.org/10.2514/1.j053907.

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33

Liangsong, Chen, He Yansong, Niu Xiyuan, Bao Jian, and Li Wei. "An alternating iterative algorithm for sound source identification based on equivalent source method." Noise Control Engineering Journal 68, no. 1 (January 20, 2020): 59–71. http://dx.doi.org/10.3397/1/37685.

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Near-field acoustical holography (NAH) based on equivalent source method (ESM) is an efficient technique for sound source identification. Conventional ESM with Tikhonov regularization (TRESM), ESM based on CVX MATLAB toolbox (CVX) and wideband acoustic holography (WBH) are commonly used methods for calculating equivalent source strengths. However, all of them have their respective limitations. To address some of these, an alternating iterative algorithm for sound source identification based on equivalent source method (AIESM) is proposed in this article, which is a combination of alternating direction method and a non-monotone line search technique. The method makes use of sparse regularization under the principle of compressive sensing (CS) to calculate equivalent source strengths. Moreover, inspired by the idea of functional beamforming (FB), AIESM with order n can yield an improved dynamic range when detecting the source location. Numerical simulations are carried out at different frequencies, and the results suggest that the computational efficiency of the proposed method is close to that of TRESM. In addition, AIESM has a better reconstruction accuracy than TRESM and WBH in a relatively wide frequency range. Compared with ESM based on CVX, AIESM is slightly better in reconstruction accuracy and has a higher computational efficiency. Meanwhile, AIESM with order n can provide more accurate source position and better resolution. The validity and practicality of the proposed method are further supported by experimental results.

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34

Fenyvesi, Bence, and Csaba Horváth. "Identification of Turbomachinery Noise Sources via Processing Beamforming Data Using Principal Component Analysis." Periodica Polytechnica Mechanical Engineering 66, no. 1 (December 22, 2021): 32–50. http://dx.doi.org/10.3311/ppme.18555.

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Complex turbomachinery systems produce a wide range of noise components. The goal is to identify noise source categories, determine their characteristic noise patterns and locations. Researchers can then use this information to quantify the impact of these noise sources, based on which new design guidelines can be proposed. Phased array microphone measurements processed with acoustic beamforming technology provide noise source maps for pre-determined frequency bands (i.e., bins) of the investigated spectrum. However, multiple noise generation mechanisms can be active in any given frequency bin. Therefore, the identification of individual noise sources is difficult and time consuming when using conventional methods, such as manual sorting. This study presents a method for combining beamforming with Principal Component Analysis (PCA) methods in order to identify and separate apart turbomachinery noise sources with strong harmonics. The method is presented through the investigation of Counter-Rotating Open Rotor (CROR) noise sources. It has been found that the proposed semi-automatic method was able to extract even weak noise source patterns that repeat throughout the data set of the beamforming maps. The analysis yields results that are easy to comprehend without special prior knowledge and is an effective tool for identifying and localizing noise sources for the acoustic investigation of various turbomachinery applications.

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35

Gandhi, Pratik, Nathan Uhunsere, Adonai Paul, Kavitha Chandra, and Charles Thompson. "Exploiting spatio-temporal spectral features of the indoor acoustic field for sound source localization." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A232. http://dx.doi.org/10.1121/10.0011160.

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The estimation and classification of spectral features of the spatio-temporal acoustic field in a reverberant rectangular enclosure is undertaken with application to localizing sound sources. The field is constructed using an image source model of the impulse response that captures the acoustic properties of the enclosure boundaries. The received field is examined considering linear and randomly distributed array configurations and spectral peaks are characterized to derive a set of features for classification and source identification. The effects of noise generated by the environment and the diffuse field from multiple reflections is distinguished.

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36

Spencer, Andrew, Keith Worden, and Gareth Pierce. "A Method for Acoustic Emission Source Identification Based on Optimisation." Key Engineering Materials 413-414 (June 2009): 793–801. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.793.

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When a metal or composite structure begins to fail, for example due to high cycle fatigue, acoustic emissions caused by the propagation of cracks give rise to bursts of ultrasonic waves travelling through the structure. The health of a structure can be monitored by means of sensors which detect these waves. Acoustic emissions are often generated in experiments by breaking a pencil lead against the surface of the structure in a standardised way but the forces that this imparts are not well understood at present. A Local Interaction Simulation Approach (LISA) algorithm has been implemented to simulate the propagation of ultrasonic waves. This code has been validated against experiments in previous work and has been shown to accurately reproduce the propagation of Lamb waves (including reflections and dispersion etc.) within thin-plate like structures. This paper deals with the use of the LISA code to characterise the forces associated with standard pencil lead breaks. The displacement due to waves emanating from a break is measured and a Differential Evolution (DE) optimisation scheme is used to find the optimal profile of forcing to match the simulation with experiment.

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37

Panjsetooni, Alireza, Norazura Muhamad Bunnori, and Amir Hossein Vakili. "Damage Source Identification of Reinforced Concrete Structure Using Acoustic Emission Technique." Scientific World Journal 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/870585.

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Acoustic emission (AE) technique is one of the nondestructive evaluation (NDE) techniques that have been considered as the prime candidate for structural health and damage monitoring in loaded structures. This technique was employed for investigation process of damage in reinforced concrete (RC) frame specimens. A number of reinforced concrete RC frames were tested under loading cycle and were simultaneously monitored using AE. The AE test data were analyzed using the AE source location analysis method. The results showed that AE technique is suitable to identify the sources location of damage in RC structures.

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38

Derouiche, Abbassia, Nacer Hamzaoui, and Taoufik Boukharouba. "Localization and Identification of Vibroacoustic Sources of Gear Transmission Mechanism by Inverse Frequency Response Function." Applied Mechanics and Materials 232 (November 2012): 437–44. http://dx.doi.org/10.4028/www.scientific.net/amm.232.437.

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Our contribution in this work is to detect, localize and quantify the noise sources radiated by a spur gear transmission mechanism. The imaging technique is used; it is based on the acoustic inverse frequency response function (IFRF). The IFRF is based on the inversion of the transfer matrix built between the source points represented by their complex source strengths and listening points represented by the complex pressures measured by the hologram. The measurements were performed in a semi-anechoic room where the floor is concrete and the walls are covered with glass wool. The complex acoustic pressures are measured by an antenna with microphones regularly spaced; it is placed above the noisy mechanism. The reconstruction problem is therefore an inverse problem and is said ill-posed; thus, regularizations are needed to stabilize and to find the best solutions. As regularization technique, the Tikhonov method is applied and the regularization parameters are chosen according to the L-curve method. The goal is to reconstruct as accurately as possible the acoustic field radiated by the transmission mechanism on a fictive and tangent plane to the noisy mechanism considered open and sometimes closed. The results obtained showed that the sources were located with good approximation. The IFRF method is able to reconstruct the sound sources responsible for the noise radiated by the mechanism without any a priori information of the sources distribution, and the visualization of spatial acoustic fields facilitate the understanding of the complex phenomena of radiation.

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39

Xiaopeng, FAN, YU Lichao, CHU Zhigang, YANG Yang, and LI Li. "Two-dimensional Dynamic Grid Compressive Beamforming for Acoustic Source Identification." Journal of Mechanical Engineering 56, no. 22 (2020): 46. http://dx.doi.org/10.3901/jme.2020.22.046.

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40

Hugo, Ronald J., Scott R. Nowlin, Ila L. Hahn, Frank D. Eaton, and Kim A. McCrae. "Acoustic Noise-Source Identification in Aircraft-Based Atmospheric Temperature Measurements." AIAA Journal 40, no. 7 (July 2002): 1382–87. http://dx.doi.org/10.2514/2.1798.

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41

Jones, Lloyd E., Neil D. Sandham, and Richard D. Sandberg. "Acoustic Source Identification for Transitional Airfoil Flows Using Cross Correlations." AIAA Journal 48, no. 10 (October 2010): 2299–312. http://dx.doi.org/10.2514/1.j050345.

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42

Bezemek, Jacklyn D., and Luc Mongeau. "Source deconvolution and system identification of flow‐excited acoustic systems." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3188–89. http://dx.doi.org/10.1121/1.419253.

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43

Presezniak, Flavio, Paulo A. G. Zavala, Gunther Steenackers, Karl Janssens, Jose R. F. Arruda, Wim Desmet, and Patrick Guillaume. "Acoustic source identification using a Generalized Weighted Inverse Beamforming technique." Mechanical Systems and Signal Processing 32 (October 2012): 349–58. http://dx.doi.org/10.1016/j.ymssp.2012.06.019.

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44

Hugo, R. J., S. R. Nowlin, I. L. Hahn, F. D. Eaton, and K. A. McCrae. "Acoustic noise-source identification in aircraft-based atmospheric temperature measurements." AIAA Journal 40 (January 2002): 1382–87. http://dx.doi.org/10.2514/3.15206.

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45

Nakano, M. "Identification of Engine Noise Source Using Acoustic Double Holography Method." JSAE Review 16, no. 1 (January 1995): 96. http://dx.doi.org/10.1016/0389-4304(95)94708-u.

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46

Shang, Yujun, Chenglong Wang, Hongmei Xu, Shuang Liu, Wei Jiang, and Jiajun Dong. "Noise Source Identification of the Grain Combining Harvester Based on Acoustic Array Test." Applied Engineering in Agriculture 36, no. 6 (2020): 879–90. http://dx.doi.org/10.13031/aea.14153.

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HIGHLIGHTSWe tested the noise of a grain combining harvester using a spiral acoustic array, aiming to identify its main sources and reduce its noise level.The noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz.When the power of other devices is cut off, engine is the main noise source. While all devices are in normal working condition, the main source of noise is the header device and the intermediate conveying device.Abstract. The grain combine harvester is an important agricultural equipment with multiple functions of harvesting, threshing, separating, cleaning and grain gathering. As an instantaneous physical pollution, noise has become one of the main causes of modern civilization diseases. The noise generated by the operation of harvesters not only causes harm to the workers, but also leads to environmental noise pollution. Here, we tested the noise of a grain combine harvester using a spiral acoustic array, aiming to identify its main source by noise source identification technology based on the sound pressure distribution and reduce its noise level. The test results show that the noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz. When the power of other devices is cut off, the engine is the main noise source, while under normal working conditions of all devices, the main source of noise is the header device and the intermediate conveying device on the front side of the harvester, the threshing device on the rear side, the engine and the threshing device on the left side, and the engine and the header device on the right side. Keywords: Acoustic array technology, Grain combining harvester, Noise source identification, Vibration and noise reduction.

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47

Uda, Toki, and Tsugutoshi Kawaguchi. "Determination of railway noise contribution based on noise source identification and acoustic transfer function." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 8 (November 30, 2023): 369–76. http://dx.doi.org/10.3397/in_2023_0068.

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Environmental noise standards of noise for Japan's bullet trains, known as Shinkansen, are among the most stringent in the world. To reduce train noise and meet these standards, the contribution ratio of each noise source to the total noise at the observation point provisioned by the Japanese government, 25 m from the nearest track must be determined. Although previous studies have determined the contribution ratio using multiple omnidirectional microphones and a 1-dimensional microphone array positioned at the observation point, we propose a new method to evaluate the contribution ratio by combining the noise source identification with an acoustic experiment. The microphone array clearly measures the noise source distribution around a vehicle using signal processing of the deconvolution algorithm, indicating that bogies and pantographs are the dominant aerodynamic noise sources. The transfer function of the radiated sound between the sources and observation point was determined in the acoustic model experiment with a viaduct and noise barrier. The contribution ratio obtained should be useful in assessing the noise reduction from shape modification.

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48

Boya, Carlos, Marta Ruiz-Llata, Julio Posada, and Jose Antonio Garcia-Souto. "Identification of multiple partial discharge sources using acoustic emission technique and blind source separation." IEEE Transactions on Dielectrics and Electrical Insulation 22, no. 3 (June 2015): 1663–73. http://dx.doi.org/10.1109/tdei.2015.7116363.

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49

Cui, Min, Yang Liu, Yanbo Wang, and Pan Wang. "Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity." Electronics 11, no. 21 (November 1, 2022): 3578. http://dx.doi.org/10.3390/electronics11213578.

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Acoustic signal classification plays a central role in acoustic source identification. In practical applications, however, varieties of training data are typically inadequate, which leads to a low sample complexity. Applying classical deep learning methods to identify acoustic signals involves a large number of parameters in the classification model, which calls for great sample complexity. Therefore, low sample complexity modeling is one of the most important issues related to the performance of the acoustic signal classification. In this study, the authors propose a novel data fusion model named MFF-ResNet, in which manual design features and deep representation of log-Mel spectrogram features are fused with bi-level attention. The proposed approach involves an amount of prior human knowledge as implicit regularization, thus leading to an interpretable and low sample complexity model of the acoustic signal classification. The experimental results suggested that MFF-ResNet is capable of accurate acoustic signal classification with fewer training samples.

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50

Aoki, Emi, Pratik Gandhi, Flore Norceide, Kavitha Chandra, and Charles Thompson. "Model based spectral feature analysis in reverberant acoustic fields." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A45. http://dx.doi.org/10.1121/10.0018097.

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The spectral features of a reverberant acoustic field that can lead to improved source localization methods are investigated. Of particular interest are identification of deterministic and probabilistic features that characterize the environment and models of the acoustic transfer function that generate such features. For example, models that predict Schroeder’s invariant standard deviation in the pressure response at source-receiver distances where the direct sound approaches the reverberant field energy are proposed. Models examined include non-stationary Gaussian processes for the acoustic reverberant components and all-pass parametric models of the acoustic impulse response.

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