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All issues Volume 7 (2023) Acta Acust., 7 (2023) 45 References
Open Access
Issue
Acta Acust.
Volume 7, 2023
Article Number 45
Number of page(s) 16
Section Virtual Acoustics
DOI https://doi.org/10.1051/aacus/2023040
Published online 27 September 2023
  1. F. Rumsey: Surround Sound. In: A. Roginska, P. Geluso, Eds., Immersive sound, Routledge, 2017, pp. 180–220. https://doi.org/10.4324/9781315707525. [Google Scholar]
  2. S. Kim: Height Channels. In: A. Roginska, P. Geluso, Eds., Immersive Sound, Routledge, 2017, pp. 221–243. https://doi.org/10.4324/9781315707525. [Google Scholar]
  3. A. Silzle, S. George, E.A. Habets, T. Bachmann: Investigation on the quality of 3D sound reproduction. In: Proceedings of the International Conference on Spatial Audio, Detmold, Germany, 10–13 November, Verband Deutscher Tonmeister, 2011, pp. 334–341. [Google Scholar]
  4. C. Eaton, H. Lee: Subjective evaluations of three-dimensional, surround and stereo loudspeaker reproductions using classical music recordings. Acoustical Science and Technology 43, 2 (2022) 149–161. https://doi.org/10.1250/ast.43.149. [CrossRef] [Google Scholar]
  5. J. Francombe, T. Brookes, R. Mason, J. Woodcock: Evaluation of spatial audio reproduction methods (part 2): analysis of listener preference. Journal of the Audio Engineering Society 65, 3 (2017) 212–225. https://doi.org/10.17743/jaes.2016.0071. [CrossRef] [Google Scholar]
  6. C. Guastavino, B.F.G. Katz: Perceptual evaluation of multi-dimensional spatial audio reproduction. Journal of the Acoustical Society of America 116, 2 (2004) 1105–1115. https://doi.org/10.1121/1.1763973. [CrossRef] [PubMed] [Google Scholar]
  7. A. Lindau, V. Erbes, S. Lepa, H.-J. Maempel, F. Brinkman, S. Weinzierl: A Spatial Audio Quality Inventory (SAQI), Acta Acustica united with Acustica 100, 5 (2014) 984–994. https://doi.org/10.3813/aaa.918778. [CrossRef] [Google Scholar]
  8. M. Schoeffler, J. Herre: The relationship between basic audio quality and overall listening experience, The Journal of the Acoustical Society of America 140, 3 (2016) 2101–2112. https://doi.org/10.1121/1.4963078. [CrossRef] [PubMed] [Google Scholar]
  9. M. Schoeffler, A. Silzle, J. Herre: Evaluation of spatial/3D audio: basic audio quality versus quality of experience. IEEE Journal of Selected Topics in Signal Processing 11, 1 (2017) 75–88. https://doi.org/10.1109/jstsp.2016.2639325. [CrossRef] [Google Scholar]
  10. E. Hahn: Musical emotions evoked by 3D audio. In: Audio Engineering Society Conference: 2018 AES International Conference on Spatial Reproduction-Aesthetics and Science. 2018. Available at https://www.aes.org/e-lib/browse.cfm?elib=19640. [Google Scholar]
  11. S. Agrawal, S. Bech, K.D. Moor, S. Forchhammer: Influence of changes in audio spatialization on immersion in audiovisual experiences, Journal of the Audio Engineering Society 70, 10 (2022) 810–823. https://doi.org/10.17743/jaes.2022.0034. [CrossRef] [Google Scholar]
  12. J. Bergner, D. Schössow, S. Preihs, J. Peissig: Identification of discriminative acoustic dimensions in stereo, surround and 3D music reproduction. Journal of the Audio Engineering Society 71, 7/8 (2023) 420–430. https://doi.org/10.17743/jaes.2022.0071. [CrossRef] [Google Scholar]
  13. Y. Wycisk, K. Sander, R. Kopiez, F. Platz, S. Preihs, J. Peissig: Wrapped into sound: development of the immersive music experience inventory (IMEI). Frontiers in Psychology 13 (2022) 951161. https://doi.org/10.3389/fpsyg.2022.951161. [CrossRef] [PubMed] [Google Scholar]
  14. J. Berg, F. Rumsey: Verification and correlation of attributes used for describing the spatial quality of reproduced sound. In: Audio Engineering Society Conference: 19th International Conference: Surround Sound - Techniques, Technology, and Perception. 2001. Available at https://www.aes.org/e-lib/browse.cfm?elib=10057. [Google Scholar]
  15. C. Colomes, S. Le Bagousse, M. Paquier: Families of sound attributes for assessment of spatial audio. In: 129th Audio Engineering Society Convention. Audio Engineering Society. 2010. Available at https://www.aes.org/e-lib/browse.cfm?elib=15728. [Google Scholar]
  16. N. Zacharov, T.H. Pedersen: Spatial sound attributes – development of a common lexicon. In: 139th Audio Engineering Society Convention. 2015. Available at https://www.aes.org/e-lib/browse.cfm?elib=17992. [Google Scholar]
  17. F. Rumsey: Spatial quality evaluation for reproduced sound: terminology, meaning, and a scene-based paradigm. Journal of the Audio Engineering Society 50, 9 (2002) 651–666. Available at https://www.aes.org/e-lib/browse.cfm?elib=11067. [Google Scholar]
  18. C. Zhang, A. Perkis, S. Arndt: Spatial immersion versus emotional immersion, which is more immersive? In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2017. https://doi.org/10.1109/qomex.2017.7965655. [Google Scholar]
  19. S. Agrawal, A. Simon, S. Bech, K. Bærentsen, S. Forchhammer: Defining immersion: literature review and implications for research on audiovisual experiences, Journal of the Audio Engineering Society 68, 6 (2020) 404–417. https://doi.org/10.17743/jaes.2020.0039. [CrossRef] [Google Scholar]
  20. C. Jennett, A.L. Cox, P. Cairns, S. Dhoparee, A. Epps, T. Tijs, A. Walton: Measuring and defining the experience of immersion in games. International Journal of Human-Computer Studies 66, 9 (2008) 641–661. https://doi.org/10.1016/j.ijhcs.2008.04.004. [CrossRef] [Google Scholar]
  21. N.C. Nilsson, R. Nordahl, S. Serafin: Immersion revisited: a review of existing definitions of immersion and their relation to different theories of presence. Human Technology 12, 2 (2016), 108–134. https://doi.org/10.17011/ht/urn.201611174652. [CrossRef] [Google Scholar]
  22. B.G. Witmer, M.J. Singer: Measuring presence in virtual environments: a presence questionnaire. Presence: Teleoperators and Virtual Environments 7, 3 (1998) 225–240. https://doi.org/10.1162/105474698565686. [CrossRef] [Google Scholar]
  23. J. Kelly, W. Woszczyk, R. King. Are you there? A literature review of presence for immersive music reproduction. In: 149th Audio Engineering Society Convention, October 27–30, 2020. Online. Available at https://www.aes.org/e-lib/browse.cfm?elib=20926. [Google Scholar]
  24. Y. Georgiou, E.A. Kyza: The development and validation of the ARI questionnaire: An instrument for measuring immersion in location-based augmented reality settings. International Journal of Human-Computer Studies 98 (2017) 24–37. https://doi.org/10.1016/j.ijhcs.2016.09.014. [CrossRef] [Google Scholar]
  25. J.S. Bradley, G.A. Soulodre: Objective measures of listener envelopment. Journal of the Acoustical Society of America 98, 5 (1995) 2590–2597. https://doi.org/10.1121/1.413225. [CrossRef] [Google Scholar]
  26. H. Furuya, K. Fujimoto, A. Wakuda, Y. Nakano: The influence of total and directional energy of late sound on listener envelopment. Acoustical Science and Technology 26, 2 (2005) 208–211. https://doi.org/10.1250/ast.26.208. [CrossRef] [Google Scholar]
  27. J. Pätynen, T. Lokki: Perception of music dynamics in concert hall acoustics. Journal of the Acoustical Society of America 140, 5 (2016) 3787–3798. https://doi.org/10.1121/1.4967157. [CrossRef] [PubMed] [Google Scholar]
  28. G.A. Soulodre, M.C. Lavoie, S.G. Norcross: Objective measures of listener envelopment in multichannel surround systems. Journal of the Audio Engineering Society 51, 9 (2003) 826–840. Available at https://www.aes.org/e-lib/browse.cfm?elib=12284. [Google Scholar]
  29. J. Blauert: Spatial hearing: the psychophysics of human sound localization, MIT Press, 1997. [Google Scholar]
  30. M.S. Engel, A. Fiebig, C. Pfaffenbach, J. Fels: A review of the use of psychoacoustic indicators on soundscape studies. Current Pollution Reports 7, 3 (2021) 359–378. https://doi.org/10.1007/s40726-021-00197-1. [CrossRef] [Google Scholar]
  31. J. Bergner, J. Peissig: On the identification and assessment of underlying acoustic dimensions of soundscapes, Acta Acustica 6 (2022) 46. https://doi.org/10.1051/aacus/2022042. [CrossRef] [EDP Sciences] [Google Scholar]
  32. A.M. Sarroff, J.P. Bello: Toward a computational model of perceived spaciousness in recorded music. Journal of the Audio Engineering Society 59, 7/8 (2011) 498–513. Available at https://www.aes.org/e-lib/browse.cfm?elib=15975. [Google Scholar]
  33. R. Hupke, M. Nophut, S. Li, R. Schlieper, S. Preihs, J. Peissig: The immersive media laboratory: Installation of a novel multichannel audio laboratory for immersive media applications. In: 144th Audio Engineering Society Convention. 2018. Available at https://www.aes.org/e-lib/browse.cfm?elib=19522. [Google Scholar]
  34. ITU-R BS.1116-3: Methods for the subjective assessment of small impairments in audio systems. International Telecommunications Union. 2015. [Google Scholar]
  35. J. Abildgaard Pedersen, F. El-Azm: Natural timbre in room correction systems (Part II). In: The Proceeding of the Audio Engineering Society Conference: 32nd International Conference: DSP for Loudspeakers, Hillerød, Denmark, 21–23 September, Audio Engineering Society, 2007. Available at https://www.aes.org/e-lib/browse.cfm?elib=14201. [Google Scholar]
  36. ITU-R BS.2051-2: Advanced sound system for programme production, International Telecommunications Union, 2018. [Google Scholar]
  37. R. Hupke, J. Ordner, J. Bergner, M. Nophut, S. Preihs, J. Peissig: Towards a Virtual Audiovisual Environment for Interactive 3D Audio Productions. In: Audio Engineering Society Conference: 2019 AES International Conference on Immersive and Interactive Audio, York, UK, 27–29 March, Audio Engineering Society, 2019. Available at https://www.aes.org/e-lib/browse.cfm?elib=20438. [Google Scholar]
  38. D. Schössow: QUEST - Questionnaire Editor SysTem, 2022. Available at https://doi.org/10.5281/ZENODO.7360198. [Google Scholar]
  39. J.P. Bachorik, M. Bangert, P. Loui, K. Larke, J. Berger, R. Rowe, G. Schlaug: Emotion in motion: investigating the time-course of emotional judgments of musical stimuli. Music Perception 26, 4 (2009) 355–364. https://doi.org/10.1525/mp.2009.26.4.355. [CrossRef] [Google Scholar]
  40. Richard Wagner – Die Walküre. The Metropolitan Opera. Deutsche Grammophon 0734855. 2011. [Google Scholar]
  41. Pyotr Tchaikovsky - Rococo Variations for Cello and Wind Quintet. Orchestra Academy of the Bayerisches Staatsorchester. Hänssler Classic. 2018. [Google Scholar]
  42. J. Meyer, G. Elko: A highly scalable spherical microphone array based on an orthonormal decomposition of the soundfield. In: IEEE International Conference on Acoustics Speech and Signal Processing, Orlando, FL, USA, 13–17 May, IEEE, 2002. https://doi.org/10.1109/icassp.2002.5744968. [Google Scholar]
  43. C. Schörkhuber, M. Zaunschirm, R. Höldrich: Binaural rendering of ambisonic signals via magnitude least squares. In: Fortschritte der Akustik - DAGAMunich, Germany, 19–22 March, Deutsche Gesellschaft für Akustik e.V. (DEGA), 2018, pp. 339–342. [Google Scholar]
  44. B. Bernschütz: A spherical far field HRIR/HRTF compilation of the Neumann KU 100. In: Fortschritte der Akustik – AIA-DAGA 2013, Merano, Italy, 18–21 March, German Acoustical Society (DEGA), Berlin, 2013, pp. 592–595. [Google Scholar]
  45. IEM Plugin Suite: Institut für Elektronische Musik und Akustik, Universität für Musik und darstellende Kunst Graz, 2021. Available at https://plugins.iem.at. [Google Scholar]
  46. H. Lee, M. Frank, F. Zotter: Spatial and timbral fidelities of binaural Ambisonics decoders for main microphone array recordings. In: Audio Engineering Society Conference: 2019 AES International Conference on Immersive and Interactive Audio, York, UK, 27–29 March, Audio Engineering Society, 2019. Available at https://www.aes.org/e-lib/browse.cfm?elib=20392. [Google Scholar]
  47. S. Spors, H. Wierstorf, A. Raake, F. Melchior, M. Frank, F. Zotter: Spatial sound with loudspeakers and its perception: a review of the current state. Proceedings of the IEEE 101, 9 (2013) 1920–1938. https://doi.org/10.1109/jproc.2013.2264784. [CrossRef] [Google Scholar]
  48. The Mathworks Inc: Audio Toolbox. 2022. Available at https://de.mathworks.com/products/audio.html. [Google Scholar]
  49. A. Politis: Microphone array processing for parametric spatial audio techniques. PhD thesis, Department of Signal Processing and Acoustics, Aalto University, Finland, 2016. [Google Scholar]
  50. P. Majdak, C. Hollomey, R. Baumgartner: AMT 1.x: A toolbox for reproducible research in auditory modeling. Acta Acustica 6 (2022) 19. https://doi.org/10.1051/aacus/2022011. [CrossRef] [EDP Sciences] [Google Scholar]
  51. Institute of Sound Recording, University of Surrey: AudioCommons timbral models. 2019. Available at https://www.audiocommons.org/materials/. [Google Scholar]
  52. S. Olive, T. Welti, E. McMullin, Listener preferences for in-room loudspeaker and headphone target responses. In: 135th Audio Engineering Society Convention, New York, NY, 17–20 October, 2013. Available at https://www.aes.org/e-lib/browse.cfm?elib=17042. [Google Scholar]
  53. C. Zhang: The why, what, and how of immersive experience, IEEE Access 8 (2020) 90878–90888. https://doi.org/10.1109/access.2020.2993646. [CrossRef] [Google Scholar]
  54. J.O. Rawlings, S.G. Pantula, D.A. Dickey, Eds.: Applied regression analysis, Springer-Verlag, 1998. https://doi.org/10.1007/b98890. [Google Scholar]
  55. E. Cantoni, N. Jacot, P. Ghisletta: Review and comparison of measures of explained variation and model selection in linear mixed-effects models. Econometrics and Statistics (2021). https://doi.org/10.1016/j.ecosta.2021.05.005. [PubMed] [Google Scholar]
  56. A. Kuznetsova, P.B. Brockhoff, R.H.B. Christensen: lmerTest package: tests in linear mixed effects models. Journal of Statistical Software 82, 13 (2017) 1–26. https://doi.org/10.18637/jss.v082.i13. [CrossRef] [Google Scholar]
  57. D. Bates, M. Mächler, B. Bolker, S. Walker, Fitting linear mixed-effects models using lme4, Journal of Statistical Software 67, 1 (2015) 1–48. https://doi.org/10.18637/jss.v067.i01. [CrossRef] [Google Scholar]
  58. S. Welham, B. Cullis, B. Gogel, A. Gilmour, R. Thompson: Prediction in linear mixed models. Australian & New Zealand Journal of Statistics 46, 3 (2004) 325–347. https://doi.org/10.1111/j.1467-842x.2004.00334.x. [CrossRef] [Google Scholar]
  59. I. Guyon, A. Elisseeff: An introduction to variable and feature selection, Journal of Machine Learning Research 3 (2003) 1157–1182. Available at https://www.jmlr.org/papers/v3/guyon03a.html. [Google Scholar]
  60. A. Groll, G. Tutz: Variable selection for generalized linear mixed models by L1-penalized estimation, Statistics and Computing 24, 2 (2012) 137–154. https://doi.org/10.1007/s11222-012-9359-z. [Google Scholar]
  61. S. Nakagawa, H. Schielzeth: A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4, 2 (2012) 133–142. https://doi.org/10.1111/j.2041–210x.2012.00261.x. [Google Scholar]
  62. H. Akaike; A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 6 (1974) 716–723. https://doi.org/10.1109/tac.1974.1100705. [CrossRef] [Google Scholar]
  63. H. Vafaie, K. De Jong: Genetic algorithms as a tool for feature selection in machine learning. In: Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI ’92, Arlington, VA, USA, 10–13 November, IEEE. 1992, pp. 200–203. https://doi.org/10.1109/TAI.1992.246402. [Google Scholar]
  64. M. Chiesa, G. Maioli, G.I. Colombo, L. Piacentini: GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC Bioinformatics 21 (2020) 1. https://doi.org/10.1186/s12859-020-3400-6. [CrossRef] [PubMed] [Google Scholar]
  65. R. Kohavi, G.H. John: Wrappers for feature subset selection, Artificial Intelligence 97, 1–2 (1997) 273–324. https://doi.org/10.1016/s0004-3702(97)00043-x. [Google Scholar]
  66. N. Morrow-Howell: The M word: multicollinearity in multiple regression, Social Work Research 18, 4 (1994) 247–251. https://doi.org/10.1093/swr/18.4.247. [CrossRef] [Google Scholar]
  67. D. Ludecke, M. Ben-Shachar, I. Patil, P. Waggoner, D. Makowski: performance: an R package for assessment, comparison and testing of statistical models, Journal of Open Source Software 6, 60 (2021) 3139. https://doi.org/10.21105/joss.03139. [CrossRef] [Google Scholar]
  68. V. Pulkki: Spatial sound reproduction with directional audio coding. Journal of the Audio Engineering Society 55 (2007) 503–516. Available at https://www.aes.org/e-lib/browse.cfm?elib=14170. [Google Scholar]
  69. S. Klockgether, S. van de Par: A model for the prediction of room acoustical perception based on the just noticeable differences of spatial perception, Acta Acustica united with Acustica 100, 5 (2014) 964–971. https://doi.org/10.3813/aaa.918776. [CrossRef] [Google Scholar]
  70. M. Morimoto, K. Iida, Y. Furue: Relation between auditory source width in various sound fields and degree of interaural cross-correlation, Applied Acoustics 38, 2–4 (1993) 291–301. https://doi.org/10.1016/0003-682x(93)90057-d. [CrossRef] [Google Scholar]
  71. T. Hidaka, T. Okano, L. Beranek: Interaural cross correlation (IACC) as a measure of spaciousness and envelopment in concert halls, Journal of the Acoustical Society of America 92, 4 (1992) 2469–2469. https://doi.org/10.1121/1.404472. [CrossRef] [Google Scholar]
  72. J. Blauert, W. Lindemann: Auditory spaciousness: some further psychoacoustic analyses. Journal of the Acoustical Society of America 80, 2 (1986) 533–542. https://doi.org/10.1121/1.394048. [CrossRef] [PubMed] [Google Scholar]
  73. J. Catic, S. Santurette, J.M. Buchholz, F. Gran, T. Dau: The effect of interaural-level-difference fluctuations on the externalization of sound. Journal of the Acoustical Society of America 134, 2 (2013) 1232–1241. https://doi.org/10.1121/1.4812264. [CrossRef] [PubMed] [Google Scholar]
  74. A. Andreopoulou, B.F.G. Katz: Identification of perceptually relevant methods of inter-aural time difference estimation, Journal of the Acoustical Society of America 142, 2 (2017) 588–598. https://doi.org/10.1121/1.4996457. [CrossRef] [PubMed] [Google Scholar]
  75. R. Kiyan, J. Bergner, S. Preihs, Y. Wycisk, D. Schössow, K. Sander, J. Peissig, R. Kopiez: Immersive music experience in surround sound music reproduction [Data set]. Leibniz University Hannover Research Data Repository. 2023. https://doi.org/10.25835/3vx9ls5h. [Google Scholar]
  76. J. Bergner: Soundscape Analysis – Indicators [Code]. GitLab. 2023. https://gitlab.com/janywhere/sosca-indicators/. [Google Scholar]
  77. R. Kiyan: immersionmodeling [Code]. Leibniz University Hannover GitLab. 2023. https://gitlab.uni-hannover.de/roman.kiyan.jr/immersionmodeling/. [Google Scholar]

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