Issue
Acta Acust.
Volume 6, 2022
Topical Issue - Auditory models: from binaural processing to multimodal cognition
Article Number 31
Number of page(s) 18
DOI https://doi.org/10.1051/aacus/2022028
Published online 28 July 2022
  1. G. Keidser, H. Dillon, M. Flax, T. Ching, S. Brewer: The NAL-NL2 prescription procedure, Audiology Research 1, 1 (2011) 88–90. https://doi.org/10.4081/audiores.2011.e24. [CrossRef] [PubMed] [Google Scholar]
  2. B. Kollmeier, J. Kiessling: Functionality of hearing aids: State-of-the-art and future model-based solutions. International Journal of Audiology 57, sup3 (2018) S3–S28. https://doi.org/10.1080/14992027.2016.1256504. [CrossRef] [Google Scholar]
  3. R. Plomp: Auditory handicap of hearing impairment and the limited benefit of hearing aids. The Journal of the Acoustical Society of America 63, 2 (1978) 533–549. https://doi.org/10.1121/1.381753. [CrossRef] [PubMed] [Google Scholar]
  4. M. Vorländer: Auralization, Springer, Cham, 2020. [CrossRef] [Google Scholar]
  5. D. Schröder, M. Vorländer: RAVEN: A real-time framework for the auralization of interactive virtual environments, in Forum Acusticum, Aalborg, Denmark, January 2011, 1541–1546. [Google Scholar]
  6. O. Buttler, T. Wendt, S. van de Par, S.D. Ewert: Perceptually plausible room acoustics simulation including diffuse reflections. The Journal of the Acoustical Society of America 143, 3 (2018) 1829–1830. https://doi.org/10.1121/1.5036004. [CrossRef] [Google Scholar]
  7. G. Grimm, J. Luberadzka, V. Hohmann: A toolbox for rendering virtual acoustic environments in the context of audiology. Acta Acustica united with Acustica 105, 3 (2019) 566–578. https://doi.org/10.3813/AAA.919337. [CrossRef] [Google Scholar]
  8. H. Kayser, T. Herzke, P. Maanen, C. Pavlovic, V. Hohmann: Open master hearing aid (openMHA) – an integrated platform for hearing aid research. The Journal of the Acoustical Society of America 146, 4 (2019) 2879–2879. https://doi.org/10.1121/1.5136988. [CrossRef] [Google Scholar]
  9. M.R. Schädler, D. Hülsmeier, A. Warzybok, B. Kollmeier: Individual aided speech-recognition performance and predictions of benefit for listeners with impaired hearing employing FADE. Trends in Hearing 24 (2020) 2331216520938929. https://doi.org/10.1177/2331216520938929. [PubMed] [Google Scholar]
  10. M. Lavandier, S. Jelfs, J.F. Culling, A.J. Watkins, A.P. Raimond, S.J. Makin: Binaural prediction of speech intelligibility in reverberant rooms with multiple noise sources. The Journal of the Acoustical Society of America 131, 1 (2012) 218–231. https://doi.org/10.1121/1.3662075. [CrossRef] [PubMed] [Google Scholar]
  11. W.O. Olsen: Average speech levels and spectra in various speaking/listening conditions. American Journal of Audiology 7, 2 (1998) 21–25. https://doi.org/10.1044/1059-0889(1998/012). [CrossRef] [PubMed] [Google Scholar]
  12. B. Kollmeier, A. Warzybok, S. Hochmuth, M.A. Zokoll, V. Uslar, T. Brand, K.C. Wagener: The multilingual matrix test: Principles, applications, and comparison across languages: A review. International Journal of Audiology 54, Sup2 (2015) 3–16. https://doi.org/10.3109/14992027.2015.1020971. [CrossRef] [PubMed] [Google Scholar]
  13. M.R. Schädler, A. Warzybok, S.D. Ewert, B. Kollmeier: A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception. The Journal of the Acoustical Society of America 139, 5 (2016) 2708–2722. https://doi.org/10.1121/1.4948772. [CrossRef] [PubMed] [Google Scholar]
  14. M.R. Schädler, A. Warzybok, B. Kollmeier: Objective prediction of hearing aid benefit across listener groups using machine learning: Speech recognition performance with binaural noise-reduction algorithms. Trends in Hearing 22 (2018) 2331216518768954. https://doi.org/10.1177/2331216518768954. [PubMed] [Google Scholar]
  15. B. Kollmeier, M.R. Schädler, A. Warzybok, B.T. Meyer, T. Brand: Sentence recognition prediction for hearing-impaired listeners in stationary and fluctuation noise with FADE: Empowering the attenuation and distortion concept by Plomp with a quantitative processing model. Trends in Hearing 20 (2016) 2331216516655795. https://doi.org/10.1177/2331216516655795. [CrossRef] [Google Scholar]
  16. N. Bisgaard, M.S. Vlaming, M. Dahlquist: Standard audiograms for the IEC 60118–15 measurement procedure. Trends in Amplification 14, 2 (2010) 113–120. https://doi.org/10.1177/1084713810379609. [CrossRef] [PubMed] [Google Scholar]
  17. A.W. Bronkhorst: The cocktail party phenomenon: A review of research on speech intelligibility in multiple-talker conditions. Acta Acustica united with Acustica 86, 1 (2000) 117–128. [Google Scholar]
  18. J. Rennies, V. Best, E. Roverud, G. Kidd Jr: Energetic and informational components of speech-on-speech masking in binaural speech intelligibility and perceived listening effort. Trends in Hearing 23 (2019) 2331216519854597. https://doi.org/10.1177/2331216519854597. [PubMed] [Google Scholar]
  19. J.A. Grange, J.F. Culling, B. Bardsley, L.I. Mackinney, S.E. Hughes, S.S. Backhouse: Turn an ear to hear: How hearing-impaired listeners can exploit head orientation to enhance their speech intelligibility in noisy social settings. Trends in Hearing 22 (2018) 2331216518802701. https://doi.org/10.1177/2331216518802701. [CrossRef] [Google Scholar]
  20. M.R. Schädler, P. Kranzusch, C. Hauth, A. Warzybok: Simulating spatial speech recognition performance with an automatic-speech-recognition-based model, in: Proceedings of DAGA, Deutsche Gesellschaft für Akustik. 2020, pp. 908–911. [Google Scholar]
  21. R. Beutelmann, T. Brand, B. Kollmeier: Revision, extension, and evaluation of a binaural speech intelligibility model. The Journal of the Acoustical Society of America 127, 4 (2010) 2479–2497. https://doi.org/10.1121/1.3295575. [CrossRef] [PubMed] [Google Scholar]
  22. T. Vicente, M. Lavandier, J.M. Buchholz: A binaural model implementing an internal noise to predict the effect of hearing impairment on speech intelligibility in non-stationary noises. The Journal of the Acoustical Society of America 148, 5 (2020) 3305–3317. https://doi.org/10.1121/10.0002660. [CrossRef] [PubMed] [Google Scholar]
  23. M.R. Schädler: m-r-s/fade-tascar-openmha: Release of version used for “interactive spatial speech recogition maps” [Data set]. Zenodo, 2021. https://doi.org/10.5281/zenodo.4651595. [Google Scholar]
  24. T. Nuesse, B. Wiercinski, T. Brand, I. Holube: Measuring speech recognition with a matrix test using synthetic speech. Trends in Hearing 23 (2019) 2331216519862982. https://doi.org/10.1177/2331216519862982. [CrossRef] [Google Scholar]
  25. G. Keidser, G. Naylor, D.S. Brungart, A. Caduff, J. Campos, S. Carlile, M.G. Carpenter, G. Grimm, V. Hohmann, I. Holube, S. Launer, T. Lunner, R. Mehra, F. Rapport, M. Slaney, K. Smeds: The quest for ecological validity in hearing science: What it is, why it matters, and how to advance it. Ear and Hearing 41, Suppl 1 (2020) 5S–19S. https://doi.org/10.1097/AUD.0000000000000944. [CrossRef] [PubMed] [Google Scholar]
  26. P. Bottalico, I.I. Passione, S. Graetzer, E.J. Hunter: Evaluation of the starting point of the Lombard effect. Acta Acustica united with Acustica 103, 1 (2017) 169–172. https://doi.org/10.3813/AAA.919043. [CrossRef] [PubMed] [Google Scholar]
  27. G. Grimm, J. Luberadzka, V. Hohmann: Virtual acoustic environments for comprehensive evaluation of model-based hearing devices. International Journal of Audiology 57, sup3 (2018) S112–S117. https://doi.org/10.1080/14992027.2016.1247501. [CrossRef] [PubMed] [Google Scholar]
  28. J.A. Grange, J.F. Culling: The benefit of head orientation to speech intelligibility in noise. The Journal of the Acoustical Society of America 139, 2 (2016) 703–712. https://doi.org/10.1121/1.4941655. [CrossRef] [PubMed] [Google Scholar]
  29. H. Dillon: Hearing aids, Thieme Medical Publishers, 2012. [Google Scholar]
  30. R. Pelzer, M. Dinakaran, F. Brinkmann, S. Lepa, P. Grosche, S. Weinzierl: Head-related transfer function recommendation based on perceptual similarities and anthropometric features. The Journal of the Acoustical Society of America 148, 6 (2020) 3809–3817. https://doi.org/10.1121/10.0002884. [CrossRef] [PubMed] [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.