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All issues Volume 5 (2021) Acta Acust., 5 (2021) 44 References
Open Access
Issue
Acta Acust.
Volume 5, 2021
Article Number 44
Number of page(s) 16
Section Computational and Numerical Acoustics
DOI https://doi.org/10.1051/aacus/2021034
Published online 20 October 2021
  1. T.J. Mueller: Aeroacoustic measurements, Springer-Verlag, 2002; Beamforming in Acoustic Testing, 62–97. [Google Scholar]
  2. R.P. Dougherty: Functional Beamforming, in: 5th Berlin Beamforming Conference, 2014, BeBeC-2014-01. [Google Scholar]
  3. R.P. Dougherty: Functional beamforming for aeroacoustic source distributions, in: 20th AIAA/CEAS Aeroacoustics Conference, AIAA 2014-3066, 2014. [Google Scholar]
  4. T.F. Brooks, W.M. Humphreys: A deconvolution approach for the mapping of acoustic sources (damas) determined from phased microphone arrays. Journal of Sound and Vibration 294 (2006) 856–879. [CrossRef] [Google Scholar]
  5. P. Sijtsma: CLEAN based on spatial source coherence. International Journal of Aeroacoustics 6, 4 (2007) 357–374. [CrossRef] [Google Scholar]
  6. T. Suzuki: L1 generalized inverse beam-forming algorithm resolving coherent/incoherent, distributed and multipole sources. Journal of Sound and Vibration 330 (2011) 5835–5851. [CrossRef] [Google Scholar]
  7. T. Yardibi, J. Li, P. Stoica, L.N. Cattafesta: Sparsity constrained deconvolution approaches for acoustic source mapping. Journal of the Acoustical Society of America 123 (2008) 2631–2642. [CrossRef] [PubMed] [Google Scholar]
  8. Q. Leclere, A. Pereira, C. Bailly, J. Antoni, C. Picard: A unified formalism for acoustic imaging based on microphone array measurements: International Journal of Aeroacoustics 164–5 (2017) 431–456. [CrossRef] [Google Scholar]
  9. G. Herold, E. Sarradj: Performance analysis of microphone array methods. Journal of Sound and Vibration 401 (2017) 152–168. [CrossRef] [Google Scholar]
  10. K. Ehrenfried, L. Koop: Comparison of iterative deconvolution algorithms for the mapping of acoustic sources. AIAA Journal 45, 7 (2007) 1584–1595. [CrossRef] [Google Scholar]
  11. Z. Chu, Y. Yang: Comparison of deconvolution methods for the visualization of acoustic sources based on cross-spectral imaging function beamforming. Mechanical System and Signal Pocessing 48 (2014) 404–422. [CrossRef] [Google Scholar]
  12. T. Yardibi, N.S. Zawodny, C. Bahr, F. Liu, L.N. Cattafesta, J. Li: Comparison of microphone array processing techniques for aeroacoustic measurements. International Journal of Aeroacoustics 9, 6 (2010) 733–761. [CrossRef] [Google Scholar]
  13. R. Merino-Martínez, P. Sijtsma, M. Snellen, T. Ahlefeldt, J. Antoni, C.J. Bahr, D. Blacodon, D. Ernst, A. Finez, S. Funke, T.F. Geyer, S. Haxter, G. Herold, X. Huang, W.M. Humphreys, Q. Leclère, A. Malgoezar, U. Michel, T. Padois, A. Pereira, C. Picard, E. Sarradj, H. Siller, D.G. Simons, C Spehr: A review of acoustic imaging methods using phased microphone arrays. CEAS Aeronautical Journal 10 (2019) 197–230. [CrossRef] [Google Scholar]
  14. P. Chiariotti, M. Martarelli, P. Castellini: Acoustic beamforming for noise source localization: Reviews, methodology and applications. Mechanical Systems and Signal Processing 120 (2019) 422–448. [CrossRef] [Google Scholar]
  15. E. Sarradj: Three-dimensional acoustic sourcemapping with different beamforming steering vector formulations. Advances in Acoustics and Vibration 2012 (2012) 292695. [CrossRef] [Google Scholar]
  16. J. Fischer, C. Doolan: Beamforming in a reverberant environment using numerical and experimental steering vector formulations. Mechanical Systems and Signal Processing 91 (2017) 10–22. [CrossRef] [Google Scholar]
  17. E. Sarradj: Quantitative source spectra from acoustic array measurements, in: 2nd Berlin Beamforming Conference, 2008, BeBeC-2008-03. [Google Scholar]
  18. E. Sarradj, T. Geyer, H. Brick, K.-R. Kirchner, T. Kohrs: Application of beamforming and deconvolution techniques to aeroacoustic sources at highspeed trains, in: NOVEM – Noise and Vibration: Emerging Methods, Sorrento, 2012. [Google Scholar]
  19. R. Merino-Martínez, P. Sijtsma, A.R. Carpio, R. Zamponi, S. Luesutthiviboon, A.M.N. Malgoezar, M. Snellen, C. Schram, D.G. Simons: Integration methods for distributed sound sources. International Journal of Aeroacoustics 18, 4–5 (2019) 444–469. [CrossRef] [Google Scholar]
  20. M. Kaltenbacher, B. Kaltenbacher, S. Gombots: Inverse scheme for acoustic source localization using microphone measurements and finite element simulations. Acta Acustica united with Acustica 104 (2018) 647–656. [CrossRef] [Google Scholar]
  21. E. Deckers, S. Jonckheere, D. Vandepitte, W. Desmet: Modelling techniques for vibro-acoustic dynamics of poroelastic materials. Archives of Computational Methods in Engineering 22, 2 (2015) 183–236. [CrossRef] [Google Scholar]
  22. S.W. Anzengruber, B. Hofmann, P. Mathé: Regularization properties of the sequential discrepancy principle for Tikhonov regularization in Banach spaces. Applicable Analysis 93, 7 (2014) 1382–1400. [CrossRef] [Google Scholar]
  23. M. Kaltenbacher: Numerical simulation of mechatronic sensors and actuators: Finite elements for computational multiphysics, 3rd ed. Springer, 2015. [Google Scholar]
  24. I.M. Sobol’: Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling and Computational Experiments 1 (1993) 407–414. [Google Scholar]
  25. S. Gombots: Acoustic source localization at low frequencies using microphone arrays. PhD Thesis, TU Wien, 2020. [Google Scholar]
  26. J. Nossent, P. Elsen, W. Bauwens: Sobol sensitivity analysis of a complex environmental model. Environmental Modelling & Software 26, 12 (2011) 1515–1525. [CrossRef] [Google Scholar]
  27. B. Iooss, P. Lemaître: A review on global sensitivity analysis methods, in: Uncertainty management in simulation-optimization of complex systems: Algorithms and applications, C. Meloni, G. Dellino, Editors. Springer, 2015. [Google Scholar]
  28. T. Homma, A. Saltelli: Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering & System Safety 52, 1 (1996) 1–17. [CrossRef] [Google Scholar]
  29. F. Pianosi, F. Sarrazin, T. Wagener: A Matlab toolbox for global sensitivity analysis. Environmental Modelling & Software 70 (2015) 80–85. [CrossRef] [Google Scholar]
  30. S. Tarantola, W. Becker: SIMLAB software for uncertainty and sensitivity analysis, in: Handbook of Uncertainty Quantification, R. Ghanem, D. Higdon, H. Owhadi, Editors. Cham, Springer International Publishing, 2017, pp. 1695–1731. [Google Scholar]
  31. C. Tong: Problem solving environment for uncertainty analysis and design exploration, in: Handbook of Uncertainty Quantification, R. Ghanem, D. Higdon, H. Owhadi, Editors. Cham, Springer International Publishing, 2017, pp. 1695–1731. [CrossRef] [Google Scholar]
  32. Y. Gan, Q. Duan, W. Gong, C. Tong, Y. Sun, W. Chu, A. Ye, C. Miao, Z. Di: A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model. Environmental Modelling & Software 51 (2014) 269–285. [CrossRef] [Google Scholar]
  33. T. Ziehn, A. Tomlin: GUI-HDMR – A software tool for global sensitivity analysis of complex models. Environmental Modelling & Software 24 (2009) 775–785. [CrossRef] [Google Scholar]
  34. J. Herman, W. Usher, SA Lib: An open-source python library for sensitivity analysis. The Journal of Open Source Software 2, 9 (2017) 97. [CrossRef] [Google Scholar]
  35. A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto: Sensitivity analysis in practice: a guide to assessing scientific models, 2nd ed. Wiley Online Library, 2017. [Google Scholar]
  36. X.-Y. Zhang, M. Trame, L. Lesko, S. Schmidt: Sobol sensitivity analysis: A tool to DE the development and evaluation of systems pharmacology model. CPT: Pharmacometrics & Systems, Pharmacology 4, 2 (2015) 69–79. [Google Scholar]
  37. A. Saltelli: Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications 145 (2002) 280–297. [NASA ADS] [CrossRef] [Google Scholar]
  38. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola: Global sensitivity analysis. The Primer. John Wiley & Sons, 2008. [Google Scholar]
  39. B. Efron, R. Tibshirani: An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993. [Google Scholar]
  40. R.W. Johnson: An Introduction to the Bootstrap. Teaching Statistics 23, 2 (2001) 49–54. [CrossRef] [Google Scholar]
  41. ISO 10534–2: Acoustics – Determination of sound absorption coefficient and impedance in impedance tubes – Part 2: Transfer-function method, 2001. [Google Scholar]
  42. M.E. Delany, E.N. Bazley: Acoustical properties of fibrous absorbent materials. Applied Acoustics 3, 2 (1970) 105–116. [Google Scholar]
  43. M.E. Delany, E.N. Bazley: Acoustical properties of porous materials. Modifications of Delany-Bazley models. Journal of the Acoustical Society of Japan 11, 1 (1990) 19–24. [Google Scholar]
  44. ISO 9053–1: Acoustics – Determination of airflow resistance – Part 1: Static airflow method, 2019. [Google Scholar]
  45. J.F. Allard, N. Atalla: Propagation of sound in porous media: modelling sound absorbing materials. 1st ed. John Wiley & Sons, 2009. [CrossRef] [Google Scholar]
  46. K. Levenberg: A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2, 2 (1944) 164–168. [CrossRef] [Google Scholar]
  47. D.W. Marquardt: An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11, 2 (1963) 431–441. [CrossRef] [Google Scholar]
  48. B. Hofmann-Wellenhof, H. Lichtenegger, J. Collins: Global positioning system: theory and practice. Springer Science & Business Media, 2012. [Google Scholar]
  49. J. Chen, J. Benesty, Y. Huang: Time delay estimation in room acoustic environments: an overview. EURASIP Journal on Advances in Signal Processing 2006, 026503 (2006) 1–19. [Google Scholar]
  50. D.A. Bohn: Environmental effects on the speed of sound. Journal of the Audio Engineering Society 36, 4 (1988) 223–231. [Google Scholar]
  51. C. Antoine: Thermodynamique – Tensions des vapeurs. Nouvelle relation entre les tensions et les températures. Comptes Rendus des Séances de l’Académie des Sciences 107 (1888) 681–684. [Google Scholar]
  52. M.S. Brandstein, H.F. Silverman: A robust method for speech signal time-delay estimation in reverberant rooms. IEEE International Conference on Acoustics, Speech, and Signal Processing 1 (1997) 375–378. [Google Scholar]
  53. L. Cheng, C. Wu, Y. Zhang, H. Wu, M. Li, C. Maple: A survey of localization in wireless sensor network. International Journal of Distributed Sensor Networks (2012). [Google Scholar]
  54. J.M. Fresno, G. Robles, J. Martínez-Tarifa, B.G. Stewart: Survey on the Performance of Source Localization Algorithms. Sensors (Basel, Switzerland) 17, 11 (2017) 2666. [CrossRef] [Google Scholar]
  55. M. Cobos, F. Antonacci, A. Alexandridis, A. Mouchtaris, B. Lee: A survey of sound source localization methods in wireless acoustic sensor networks. Wireless Communications and Mobile Computing 2017 (2017) 1–24. [Google Scholar]
  56. R.E. Kalman: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82, 1 (1960) 35–45. [CrossRef] [Google Scholar]
  57. D. Simon: Optimal State Estimation. Wiley-Interscience, 2006. [CrossRef] [Google Scholar]
  58. T.F. Brooks, W.M. Humphreys: Effect of directional array size on the measurement of airframe noise components, in: 5th AIAA/CEAS Aeroacoustics Conference and Exhibit. AIAA, 1999. [Google Scholar]
  59. P. Sijtsma: Phased array beamforming applied to wind tunnel and fly-over tests, in: SAE Brasil International Noise and Vibration Congress, October 2010. [Google Scholar]
  60. R. Merino-Martínez, E. Neri, M. Snellen, J. Kennedy, D. Simons, G.J. Bennett: Comparing flyover noise measurements to full-scale nose landing gear wind tunnel experiments for regional aircraft, in: 23rd AIAA/CEAS Aeroacoustics Conference. AIAA 2017-3006, 2017. [Google Scholar]

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