EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 9 (2025) Acta Acust., 9 (2025) 46 References
Open Access
Issue
Acta Acust.
Volume 9, 2025
Article Number 46
Number of page(s) 30
Section Inverse Problems in Acoustics
DOI https://doi.org/10.1051/aacus/2025030
Published online 24 July 2025
  1. J. Maynard, E. Williams, Y. Lee: Nearfield acoustic holography: I. Theory of generalized holography and the development of NAH. The Journal of the Acoustical Society of America 78 (1985) 1395–1413 [Google Scholar]
  2. H. Fleischer: Fourier-Akustik: Beschreibung der Schallstrahlung von ebenen Schwingern mit Hilfe der räumlichen Fourier-Methode. VDI Verlag Düsseldorf, 1988 [Google Scholar]
  3. J. Hald: Basic theory and properties of statistically optimized near-field acoustical holography. The Journal of the Acoustical Society of America 125, 05 (2009) 2105–2120 [Google Scholar]
  4. Z. Wang, S. Wu: Helmholtz equation-least-squares method for reconstructing acoustic pressure field. The Journal of the Acoustical Society of America 102, 10 (1997) 2020–2032 [Google Scholar]
  5. A. Sarkissian: Method of superposition applied to patch near-field acoustic holography. The Journal of the Acoustical Society of America 118, 08 (2005) 671–678 [Google Scholar]
  6. W.A. Veronesi, J.D. Maynard: Digital holographic reconstruction of sources with arbitrarily shaped surfaces. The Journal of the Acoustical Society of America 85, 2 (1989) 588–598 [Google Scholar]
  7. B.-K. Kim, J. Ih: On the reconstruction of the vibro-acoustic field over the surface enclosing an interior space using the boundary element method. The Journal of the Acoustical Society of America 100, 11 (1996) 3003–3016 [Google Scholar]
  8. A. Schuhmacher, J. Hald, K. Rasmussen, P.C. Hansen: Sound source reconstruction using inverse boundary element calculations. The Journal of the Acoustical Society of America 113, 01 (2003) 114–127 [Google Scholar]
  9. N. Valdivia, E. Williams: Implicit methods of solution to integral formulations in boundary element method based nearfield acoustic holography. The Journal of the Acoustical Society of America 116, 09 (2004) 1559–1572 [Google Scholar]
  10. P. Hansen: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. SIAM Monographs of Mathematical Modeling and Computation, Society for Industrial and Applied Mathematics, 1987 [Google Scholar]
  11. G.H. Golub, P.C. Hansen, D.P. O`Leary: Tikhonov regularization and total least squares. SIAM Journal on Matrix Analysis and Applications 21 (1999) 185–194 [Google Scholar]
  12. M. Elad: Sparse and Redundant Representations – From Theory to Applications in Signal and Image Processing. Springer, 2010 [Google Scholar]
  13. E.J. Candès, M.B. Wakin: An introduction to compressive sampling. IEEE Signal Processing Magazine 25, 2 (2008) 21–30 [Google Scholar]
  14. S. Foucart, H. Rauhut: A mathematical introduction to compressive sensing, in: Applied and Numerical Harmonic Analysis. Birkhäuser New York, 2013 [Google Scholar]
  15. H. Boche, R. Calderbank, G. Kutyniok, J. Vybíral, Eds.: Compressed sensing and its applications, in: Applied and Numerical Harmonic Analysis. Birkhäuser Cham, 2015 [Google Scholar]
  16. D. Donoho: Compressed sensing. IEEE Transactions on Information Theory 52, 4 (2006) 1289–1306 [Google Scholar]
  17. S. Gade, J. Hald, K. Ginn: Wideband acoustical holography. Sound and Vibration Magazine 50, 04 (2016) 8–13 [Google Scholar]
  18. D.-Y. Hu, H.-B. Li, Y. Hu, Y. Fang: Sound field reconstruction with sparse sampling and the equivalent source method. Mechanical Systems and Signal Processing 108, 08 (2018) 317–325 [Google Scholar]
  19. C.-X. Bi, Y. Liu, L. Xu, Y.-B. Zhang: Sound field reconstruction using compressed modal equivalent point source method. The Journal of the Acoustical Society of America 141, 01 (2017) 73–79 [Google Scholar]
  20. N.M. Abusag, D.J. Chappell: On sparse reconstructions in near-field acoustic holography using the method of superposition. Journal of Computational Acoustics 24, 09 (2016) 1650009 [Google Scholar]
  21. G. Chardon, L. Daudet, A. Peillot, F. Ollivier, N. Bertin, R. Gribonval: Near-field acoustic holography using sparse regularization and compressive sampling principles. The Journal of the Acoustical Society of America 132, 09 (2012) 1521–1534 [Google Scholar]
  22. J. Hald: Fast wideband acoustical holography. The Journal of the Acoustical Society of America 139, 04 (2016) 1508–1517 [Google Scholar]
  23. C.-X. Bi, Y. Liu, Y.-B. Zhang, L. Xu: Sound field reconstruction using inverse boundary element method and sparse regularization. The Journal of the Acoustical Society of America 145, 05 (2019) 3154–3162 [Google Scholar]
  24. C.-X. Bi, Y. Liu, Y.-B. Zhang, L. Xu: Extension of sound field separation technique based on the equivalent source method in a sparsity framework. Journal of Sound and Vibration 442, 03 (2019) 125–137 [Google Scholar]
  25. E. Fernandez-Grande, L. Daudet: Compressive acoustic holography with block-sparse regularization. The Journal of the Acoustical Society of America 143, 06 (2018) 3737–3746 [Google Scholar]
  26. J. Liu, Y. Liu, J.S. Bolton: Acoustic source reconstruction and visualization based on acoustic radiation modes. Journal of Sound and Vibration 437, 09 (2018) 358–372 [Google Scholar]
  27. E. Fernandez-Grande, A. Xenaki, P. Gerstoft: A sparse equivalent source method for near-field acoustic holography. The Journal of the Acoustical Society of America 141, 01 (2017) 532–542 [Google Scholar]
  28. Y. He, L. Chen, Z. Xu, Z. Zhang: A compressed equivalent source method based on equivalent redundant dictionary for sound field reconstruction. Applied Sciences 9, 02 (2019) 808 [Google Scholar]
  29. J. Hald: A comparison of iterative sparse equivalent source methods for near-field acoustical holography. The Journal of the Acoustical Society of America 143, 06 (2018) 3758–3769 [Google Scholar]
  30. Y.-B. Zhao: Sparse Optimization Theory and Methods, 1 edn. CRC Press, 2018 [Google Scholar]
  31. M.R. Bai, J.-H. Lin, K.-L. Liu: Optimized microphone deployment for near-field acoustic holography: to be, or not to be random, that is the question. Journal of Sound and Vibration 329, 07 (2010) 2809–2824 [Google Scholar]
  32. Y. Liu, C.-X. Bi, L. Xu, Y.-B. Zhang: Sound field reconstruction using equivalent source method based on a redundant dictionary, in: INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering, 2017, pp. 1996–2998 [Google Scholar]
  33. M. Pagavino: Derivative-based regularization of inverse problems in acoustic holography. Master's thesis, University of Music and Performing Arts Graz – Institute of Electronic Music and Acoustics, 2019 [Google Scholar]
  34. I. Goodfellow, Y. Bengio, A. Courville: Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org [Google Scholar]
  35. L. Xu, J. Ren, C. Liu, J. Jia: Deep convolutional neural network for image deconvolution. Advances in Neural Information Processing Systems 2, 01 (2014) 1790–1798 [Google Scholar]
  36. O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, J. Matas: DeblurGAN: blind motion deblurring using conditional adversarial networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 [Google Scholar]
  37. J. Xie, L. Xu, E. Chen: Image denoising and inpainting with deep neural networks, in: Advances in Neural Information Processing Systems 25, 2012 [Google Scholar]
  38. C. Dong, C.C. Loy, K. He, X. Tang: Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 06 (2015) 295–307 [Google Scholar]
  39. M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, R. Baraniuk: Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine 25, 04 (2008) 83–91 [Google Scholar]
  40. E. Candès, J. Romberg, T. Tao: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59, 08 (2006) 1207–1223 [CrossRef] [Google Scholar]
  41. M. Olivieri, M. Pezzoli, R. Malvermi, F. Antonacci, A. Sarti: Near-field acoustic holography analysis with convolutional neural networks, in: INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Seoul, Korea, August 2020. Institute of Noise Control Engineering, Vol. 261 (2020) 5607–5618 [Google Scholar]
  42. M. Olivieri, M. Pezzoli, F. Antonacci, A. Sarti: Near field acoustic holography on arbitrary shapes using convolutional neural network, in: 2021 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 121–125 [Google Scholar]
  43. J. Wang, Z. Zhang, Y. Huang, Z. Li, Q. Huang: A 3D convolutional neural network based near-field acoustical holography method with sparse sampling rate on measuring surface. Measurement 177 (2021) 109297 [Google Scholar]
  44. J. Wang, Z. Zhang, Z. Li, Q. Huang: Research on joint training strategy for 3D convolutional neural network based near-field acoustical holography with optimized hyperparameters. Measurement 202 (2022) 111790 [Google Scholar]
  45. J. Wang, W. Zhang, Z. Zhang, Y. Huang: A cylindrical near-field acoustical holography method based on cylindrical translation window expansion and an autoencoder stacked with 3D-CNN layers. Sensors 23, 04 (2023) 4146 [Google Scholar]
  46. Z. Cao, L. Geng, J. Wang: Sparse-sampled near-field acoustic holography based on 3D-CNN and dense block, in: 2024 7th International Conference on Information Communication and Signal Processing (ICICSP), 2024, pp. 483–487 [Google Scholar]
  47. H. Kafri, M. Olivieri, F. Antonacci, M. Moradi, A. Sarti, S. Gannot: Grad-cam-inspired interpretation of nearfield acoustic holography using physics-informed explainable neural network, in: ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5 [Google Scholar]
  48. S. Koyama, J.G.C. Ribeiro, T. Nakamura, N. Ueno, M. Pezzoli: Physics-informed machine learning for sound field estimation: fundamentals, state of the art, and challenges. IEEE Signal Processing Magazine 41, 6, (2024) 60–71 [Google Scholar]
  49. X. Karakonstantis, E. Fernandez-Grande: Generative adversarial networks with physical sound field priors. The Journal of the Acoustical Society of America 154, 08 (2023) 1226–1238 [Google Scholar]
  50. T. Lobato, R. Sottek, M. Vorländer: Using learned priors to regularize the helmholtz equation least-squares method. The Journal of the Acoustical Society of America 155, 02 (2024) 971–983 [Google Scholar]
  51. S. Chaitanya, S. Sriraman, S. Srinivasan, K. Srinivasan: Machine learning aided near-field acoustic holography based on equivalent source method. The Journal of the Acoustical Society of America 153, 2 (2023) 940 [Google Scholar]
  52. G. Karniadakis, Y. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, L. Yang: Physics-informed machine learning. Nature Reviews Physics 3, 6 (2021) 422–440 [Google Scholar]
  53. M. Raissi, P. Perdikaris, G. Karniadakis: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378 (2019) 686–707 [Google Scholar]
  54. H. Bi, T. Abhayapala: Point neuron learning: a new physics-informed neural network architecture. EURASIP Journal on Audio, Speech, and Music Processing 2024, 1, (2024) 56 [Google Scholar]
  55. F.A. Marco Olivieri, M. Pezzoli, A. Sarti: A physics-informed neural network approach for nearfield acoustic holography. Sensors, 21, 23 (2021) 7834 [Google Scholar]
  56. X. Luan, M. Olivieri, M. Pezzoli, F. Antonacci, A. Sarti: Complex – valued physics-informed neural network for near-field acoustic holography, in: 2024 32nd European Signal Processing Conference (EUSIPCO), 2024, pp. 126–130 [Google Scholar]
  57. M. Olivieri, X. Karakonstantis, M. Pezzoli, F. Antonacci, A. Sarti, E. Fernandez-Grande: Physics-informed neural network for volumetric sound field reconstruction of speech signals. EURASIP Journal on Audio, Speech, and Music Processing 2024, 1 (2024) 42 [Google Scholar]
  58. X. Karakonstantis, D. Caviedes Nozal, A. Richard, E. Fernandez-Grande: Room impulse response reconstruction with physics-informed deep learning. The Journal of the Acoustical Society of America 155, 02 (2024) 1048–1059 [Google Scholar]
  59. X. Chen, F. Ma, A. Bastine, P. Samarasinghe, H. Sun: Sound field estimation around a rigid sphere with physics-informed neural network, in: 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023, pp. 1984–1989 [Google Scholar]
  60. F. Ma, S. Zhao, I.S. Burnett: Sound field reconstruction using a compact acoustics-informed neural network. The Journal of the Acoustical Society of America 156, 09 (2024) 2009–2021 [Google Scholar]
  61. J. Ribeiro, S. Koyama, R. Horiuchi, H. Saruwatari: Sound field estimation based on physics-constrained kernel interpolation adapted to environment. IEEE/ACM Transactions on Audio, Speech, and Language Processing PP, 01 (2024) 1–16 [Google Scholar]
  62. K. Shigemi, S. Koyama, T. Nakamura, H. Saruwatari: Physics-informed convolutional neural network with bicubic spline interpolation for sound field estimation, in: 2022 International Workshop on Acoustic Signal Enhancement (IWAENC), 2022, pp. 1–5 [Google Scholar]
  63. E. Kobler, T. Klatzer, K. Hammernik, T. Pock: Variational networks: connecting variational methods and deep learning, in: Pattern Recognition. GCPR 2017, Germany. Springer, 2017, pp. 281–293 [Google Scholar]
  64. K. Hammernik, T. Klatzer, E. Kobler, M.P. Recht, D.K. Sodickson, T. Pock, F. Knoll: Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine 79, 11 (2017) 3055–3071 [Google Scholar]
  65. Y. Chen, W. Yu, T. Pock: On learning optimized reaction diffusion processes for effective image restoration, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5261–5269 [Google Scholar]
  66. Y. Chen, T. Pock: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (2016) 1256–1272 [Google Scholar]
  67. H. Chen, Y. Zhang, Y. Chen, J. Zhang, W. Zhang, H. Sun, Y. Lv, P. Liao, J. Zhou, G. Wang: LEARN: learned experts’ assessment-based reconstruction network for sparse-data CT. IEEE Transactions on Medical Imaging 37, 6 (2018) 1333–1347 [Google Scholar]
  68. S. Roth, M.J. Black: Fields of experts. International Journal of Computer Vision 82, 04 (2009) 205–229 [Google Scholar]
  69. T. Klatzer, K. Hammernik, P. Knobelreiter, T. Pock: Learning joint demosaicing and denoising based on sequential energy minimization, in: 2016 IEEE International Conference on Computational Photography (ICCP), (Evanston, IL, USA). IEEE, 2016, pp. 1–11 [Google Scholar]
  70. A. Hirose: Complex-Valued Neural Networks. Vol. 32. Springer, 2006 [Google Scholar]
  71. J.B. Mingsian, R. Bai, J.-G. Ih: Nearfield Array Signal Processing Algorithms. John Wiley & Sons, Ltd, 2013 [Google Scholar]
  72. M. Hanke, A. Neubauer, O. Scherzer: A convergence analysis of the landweber iteration for nonlinear ill-posed problems. Numerische Mathematik 72 (1995) 21–37 [Google Scholar]
  73. M. Benzi: Preconditioning techniques for large linear systems: a survey. Journal of Computational Physics 182 (2002) 418–477 [Google Scholar]
  74. L. Rudin, S. Osher, E. Fatemi: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60 (1992) 259–268 [Google Scholar]
  75. K. Bredies, K. Kunisch, T. Pock: Total Generalized Variation. SIAM Journal on Imaging Sciences 3, 3, (2010) 492–526 [Google Scholar]
  76. V. Vishnevskiy, J. Walheim, S. Kozerke: Deep variational network for rapid 4D flow MRI reconstruction. Nature Machine Intelligence 2, 4 (2020) 228–235 [Google Scholar]
  77. V. Vishnevskiy, R. Rau, O. Goksel: Deep variational networks with exponential weighting for learning computed tomography, in: Medical Image Computing and Computer Assisted InterventionMICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22. Springer International Publishing, 2019, pp. 310–318 [Google Scholar]
  78. V. Vishnevskiy, S.J. Sanabria, O. Goksel: Image reconstruction via variational network for real-time hand-held sound-speed imaging, in: Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 1. Springer International Publishing, 2018, pp. 120–128 [Google Scholar]
  79. D. Gilton, G. Ongie, R. Willett: Neumann networks for linear inverse problems in imaging. IEEE Transactions on Computational Imaging 6 (2020) 328–343 [Google Scholar]
  80. J. Adler, O. Öktem: Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems 33 (2017) 124007 [Google Scholar]
  81. J. Xiang, Y. Dong, Y. Yang: FISTA-Net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Transactions on Medical Imaging 40, 01 (2021) 1329–1339 [Google Scholar]
  82. G. Nakerst, J. Brennan, M. Haque: Gradient descent with momentum – to accelerate or to super-accelerate? arXiv:2001.06472, (2020) [Google Scholar]
  83. L. Condat: A primal-dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms. Journal of Optimization Theory and Applications 158, 2 (2013) 460–479 [Google Scholar]
  84. B. Vu: A splitting algorithm for dual monotone inclusions involving cocoercive operators. Advances in Computational Mathematics 38, 04 (2011) 667–681 [Google Scholar]
  85. A. Chambolle, T. Pock: A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision 40, 05 (2011) 120–145 [Google Scholar]
  86. K. Hammernik: Variational networks for medical image reconstruction. PhD thesis, Technical University of Graz – Institute of Computer Graphics and Vision, 2019 [Google Scholar]
  87. N. Guberman: On complex valued convolutional neural networks. arXiv:1602.09046, (2016) [Google Scholar]
  88. S. Chen, S. McLaughlin, B. Mulgrew: Complex-valued radial basic function network, part I: Network architecture and learning algorithms. Signal Processing 35, 1 (1994) 19–31 [Google Scholar]
  89. S. Ramasamy, S. Sundaram, N. Sundararajan: A fully complex-valued radial basis function network and its learning algorithm. International Journal of Neural Systems 19, 08 (2009) 253–267 [Google Scholar]
  90. D. Caviedes-Nozal, N.A.B. Riis, F.M. Heuchel, J. Brunskog, P. Gerstoft, E. Fernandez-Grande: Gaussian processes for sound field reconstruction. The Journal of the Acoustical Society of America 149, 02 (2021) 1107–1119 [Google Scholar]
  91. I. Steinwart, A. Christmann: Support vector machines, in: Information Science and Statistics. Springer New York, New York, NY, 2008 [Google Scholar]
  92. S. Ioffe, C. Szegedy: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167, (2015) [Google Scholar]
  93. D. Ulyanov, A. Vedaldi, V. Lempitsky: Instance normalization: the missing ingredient for fast stylization. arXiv:1607.08022, (2016) [Google Scholar]
  94. C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J.F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, C.J. Pal: Deep complex networks. arXiv:1705.09792, (2017) [Google Scholar]
  95. J.S. Dramsch, M. Lüthje, A.N. Christensen: Complex-valued neural networks for machine learning on non-stationary physical data. Computers and Geosciences 146, 01 (2021) 104643 [Google Scholar]
  96. H. Vogel: A better way to construct the sunflower head. Mathematical Biosciences 44, 3 (1979) 179–189 [Google Scholar]
  97. N. Benoudjit, M. Verleysen: On the kernel widths in radial-basis function networks. Neural Processing Letters 18, 10 (2003) 139–154 [Google Scholar]
  98. J. Schwab, S. Antholzer, M. Haltmeier: Big in Japan: regularizing networks for solving inverse problems. Journal of Mathematical Imaging and Vision 62, 12 (2018) 445–455 [Google Scholar]
  99. R. Heckel: Regularizing linear inverse problems with convolutional neural networks. arXiv:1907.03100, (2019) [Google Scholar]
  100. D. Ulyanov, A. Vedaldi, V. Lempitsky: Deep image prior. International Journal of Computer Vision 128, 03 (2020) 1867–1888 [Google Scholar]
  101. D.V. Veen, A. Jalal, M. Soltanolkotabi, E. Price, S. Vishwanath, A.G. Dimakis: Compressed sensing with deep image prior and learned regularization. arXiv:1806.06438, (2018) [Google Scholar]
  102. H. Gupta, K.H. Jin, H.Q. Nguyen, M.T. McCann, M. Unser: CNN-based projected gradient descent for consistent CT image reconstruction. IEEE Transactions on Medical Imaging 37 (2018) 1440–1453 [Google Scholar]
  103. G. Mataev, M. Elad, P. Milanfar: DeepRED: deep image prior powered by RED, in: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019 [Google Scholar]
  104. S. Dittmer, T. Kluth, P. Maass, D. Otero Baguer: Regularization by architecture: a deep prior approach for inverse problems. Journal of Mathematical Imaging and Vision 62 (2020) 456–470 [Google Scholar]
  105. Z. Cheng, M. Gadelha, S. Maji, D. Sheldon: A bayesian perspective on the deep image prior, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 5443–5451 [Google Scholar]
  106. A. Sagel, A. Roumy, C. Guillemot: Sub-dip: Optimization on a subspace with deep image prior regularization and application to superresolution, in: ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 2513–2517 [Google Scholar]
  107. R. Heckel, P. Hand: Deep decoder: concise image representations from untrained non-convolutional networks. arXiv:1810.03982, (2018) [Google Scholar]
  108. Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 05 (2004) 600–612 [Google Scholar]
  109. R. Mittmannsgruber: Leistungsbasierte Analyse von Luftschallübertragungsanteilen. Master's thesis, University of Music and Performing Arts Graz – Institute of Electronic Music and Acoustics, 2017 [Google Scholar]
  110. A. Farina: Simultaneous measurement of impulse response and distortion with a swept-sine technique. Journal of the Audio Engineering Society 11 (2000) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.