About - Computational Audiology

The purpose of this website is to share knowledge and tools about computational audiology. We hope to bring experts from different disciplines such as AI and Audiology together in order to stimulate innovations for hearing impaired people anywhere. We publish blog articles about current developments, highlight ongoing projects and publications by research groups, and aim to facilitate discussion. In addition, the website is used to promote events including the virtual conference on computational audiology (VCCA) series. The VCCA2020 took place on June 19, 2020. The VCCA2021 is scheduled for June 25, 2021.

Anybody can leave a comment or share content to publish on the forum, after providing a name and valid email address. Comments only appear on the website after approval by the moderators. Content can be rejected if it is not within the scope of the forum or considered disrespectful. The tone of the forum is respectful and pleasantly informal.

Editorial Advisory Board

Dennis Barbour, PhD
Washington University in St. Louis
St. Louis, United States

Volker Hohmann, PhD
University of Oldenburg
Oldenburg, Germany

Fan-Gang Zeng, PhD
University of California
Irvine, United States

Editors

Tobias Goehring, PhD
University of Cambridge
Cambridge, United Kingdom

Jan-Willem Wasmann, MSc
Radboud Medical Center Nijmegen
Nijmegen, The Netherlands

Contributors

François Guérit, PhD
University of Cambridge
Cambridge, United Kingdom

Elle O’Brien, PhD
University of Michigan School of Information
Michigan, United States

Sigrid Polspoel, MSc
Amsterdam UMC and Vrije Universiteit Amsterdam
Amsterdam, The Netherlands

Leontien Pragt, MSc
Radboud Medical Center Nijmegen
Nijmegen, The Netherlands

Webdesigner

Iris Cedée
Iris Digitalis

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Disclaimer: the content of this website may or may not reflect the opinion of the owner. The information at computationalaudiology.com is not intended to replace the services of a trained legal or health professional. The owner and contributors specifically disclaim any liability, loss or risk, personal or otherwise, which is incurred as a consequence, directly or indirectly, of the use and application of any of the contents of this website.

Dennis Barbour on ARO Symposium Forum
marzieh.golabbakhsh on ARO Symposium Forum
Jan-Willem Wasmann on DIN-test DEMO
Lucas Mens on DIN-test DEMO
Sigrid Polspoel on Aladdin: Automatic LAnguage-independent Development of the Digits-In-Noise test

Barbour, D. L., & Wasmann, J.-W. A. (2021). Performance and Potential of Machine Learning Audiometry. The Hearing Journal, 74(3), 40–43. https://doi.org/10.1097/01.HJ.0000737592.24476.88

Kayser, H., Herzke, T., Maanen, P., Zimmermann, M., Grimm, G., & Hohmann, V. (2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). ArXiv:2103.02313 [Cs, Eess]. https://doi.org/10.5281/zenodo.4601604

Melgarejo-Nagata, H., & Cabanillas-Carbonell, M. (n.d.). Analysis of emerging technologies for the social inclusion of people with hearing disabilities: a review of the scientific literature from 2005-2020. European Journal of Molecular & Clinical Medicine, 8(03), 2021.

Melgarejo-Nagata, H., & Cabanillas-Carbonell, M. (2021). Analysis of emerging technologies for the social inclusion of people with hearing disabilities: a review of the scientific literature from 2005 - 2020. Clinical Medicine, 08(03), 14.

Zhang, Q., Wang, D., Zhao, R., & Yu, Y. (2021). SoundLip: Enabling Word and Sentence-level Lip Interaction for Smart Devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 43:1–43:28. https://doi.org/10.1145/3448087

Shekar, R. C. M. C., Belitz, C., & Hansen, J. H. L. (2021). Development of CNN-Based Cochlear Implant and Normal Hearing Sound Recognition Models Using Natural and Auralized Environmental Audio. 2021 IEEE Spoken Language Technology Workshop (SLT), 728–733. https://doi.org/10.1109/SLT48900.2021.9383550

Ratnanather, J. T., Bhattacharya, R., Heston, M. B., Song, J., Fernandez, L. R., Lim, H. S., Lee, S.-W., Tam, E., Yoo, S., Bae, S.-H., Lam, I., Jeon, H. W., Chang, S. A., & Koo, J.-W. (2021). An mHealth App (Speech Banana) for Auditory Training: App Design and Development Study. JMIR MHealth and UHealth, 9(3), e20890. https://doi.org/10.2196/20890

Sundgaard, J. V., Harte, J., Bray, P., Laugesen, S., Kamide, Y., Tanaka, C., Paulsen, R. R., & Christensen, A. N. (2021). Deep metric learning for otitis media classification. Medical Image Analysis, 102034. https://doi.org/10.1016/j.media.2021.102034

Pitathawatchai, P., Chaichulee, S., & Kirtsreesakul, V. (2021). Robust machine learning method for imputing missing values in audiograms collected in children. International Journal of Audiology, 0(0), 1–12. https://doi.org/10.1080/14992027.2021.1884909

Wasmann, J.-W. A., & Barbour, D. L. (2021). Emerging Hearing Assessment Technologies for Patient Care. The Hearing Journal, 74(3), 44. https://doi.org/10.1097/01.HJ.0000737596.12888.22

Performance and Potential of Machine Learning Audiometry : The Hearing Journal. (n.d.). Retrieved April 5, 2021, from https://journals.lww.com/thehearingjournal/Fulltext/2021/03000/Performance_and_Potential_of_Machine_Learning.11.aspx

Hülsmeier, D., Schädler, M. R., & Kollmeier, B. (2021). DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids. Hearing Research, 404, 108217. https://doi.org/10.1016/j.heares.2021.108217

Jones, S. A., & Noppeney, U. (2021). Ageing and multisensory integration: A review of the evidence, and a computational perspective. Cortex, 138, 1–23. https://doi.org/10.1016/j.cortex.2021.02.001

Kim, S. (n.d.). AI-Driven Insights from AI-Driven Data - Hearing Review. Retrieved April 5, 2021, from https://www.hearingreview.com/hearing-products/hearing-aids/ai

Uhler, K., Hunter, S., & Gilley, P. M. (2021). Mismatched response predicts behavioral speech discrimination outcomes in infants with hearing loss and normal hearing. Infancy, 26(2), 327–348. https://doi.org/https://doi.org/10.1111/infa.12386

Wimalarathna, H., Ankmnal-Veeranna, S., Allan, C., Agrawal, S. K., Allen, P., Samarabandu, J., & Ladak, H. M. (2021). Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory Processing Disorder. Computer Methods and Programs in Biomedicine, 200, 105942. https://doi.org/10.1016/j.cmpb.2021.105942

Dotan, A., & Shriki, O. (2021). Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network. BioRxiv, 2021.01.11.426188. https://doi.org/10.1101/2021.01.11.426188

Dotan, A., & Shriki, O. (2021). Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network. BioRxiv.

Wasmann, J.-W. A., & Barbour, D. L. (2021). Emerging Hearing Assessment Technologies for Patient Care. The Hearing Journal, 74(3), 44. https://doi.org/10.1097/01.HJ.0000737596.12888.22

Barbour, D. L., & Wasmann, J.-W. A. (2021). Performance and Potential of Machine Learning Audiometry. The Hearing Journal, 74(3), 40. https://doi.org/10.1097/01.HJ.0000737592.24476.88

Wasmann, J.-W. A., Lanting, C. P., Huinck, W. J., Mylanus, E. A. M., van der Laak, J. W. M., Govaerts, P. J., Swanepoel, D. W., Moore, D. R., & Barbour, D. L. (2021). Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age. Ear and Hearing, Publish Ahead of Print. https://doi.org/10.1097/AUD.0000000000001041

Headphone screening to facilitate web-based auditory experiments | SpringerLink. (n.d.). Retrieved March 14, 2021, from https://link.springer.com/article/10.3758/s13414-017-1361-2

Woods, K. J. P., Siegel, M. H., Traer, J., & McDermott, J. H. (2017). Headphone screening to facilitate web-based auditory experiments. Attention, Perception, & Psychophysics, 79(7), 2064–2072. https://doi.org/10.3758/s13414-017-1361-2

De Sousa, K. C., Swanepoel, D. W., Moore, D. R., Myburgh, H. C., & Smits, C. (2020). Improving Sensitivity of the Digits-In-Noise Test Using Antiphasic Stimuli. Ear and Hearing, 41(2), 442–450. https://doi.org/10.1097/AUD.0000000000000775

Chesley, B., & Barbour, D. L. (2020). Visual Field Estimation by Probabilistic Classification. IEEE Journal of Biomedical and Health Informatics, 24(12), 3499–3506. https://doi.org/10.1109/JBHI.2020.2999567

Motlagh Zadeh, L., Silbert, N. H., Sternasty, K., Swanepoel, D. W., Hunter, L. L., & Moore, D. R. (2019). Extended high-frequency hearing enhances speech perception in noise. Proceedings of the National Academy of Sciences, 116(47), 23753–23759. https://doi.org/https://doi.org/10.1073/pnas.1903315116

Saunders, G. H., Bott, A., & Tietz, L. H. (2020). Hearing care providers’ perspectives on the utility of datalogging information. American Journal of Audiology, 29(3S), 610–622. https://doi.org/https://doi.org/10.1044/2020_AJA-19-00089

Saunders Gabrielle H., Bott Anthea, & Tietz Lukas H. B. (2020). Hearing Care Providers’ Perspectives on the Utility of Datalogging Information. American Journal of Audiology, 29(3S), 610–622. https://doi.org/10.1044/2020_AJA-19-00089

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Masalski, M., Grysiński, T., & Kręcicki, T. (2018). Hearing tests based on biologically calibrated mobile devices: comparison with pure-tone audiometry. JMIR MHealth and UHealth, 6(1), e10.

Colsman, A., Supp, G. G., Neumann, J., & Schneider, T. R. (2020). Evaluation of Accuracy and Reliability of a Mobile Screening Audiometer in Normal Hearing Adults. Frontiers in Psychology, 11, 744.

Yao, J., Wan, Y., & Givens, G. D. (2010). Using web services to realize remote hearing assessment. Journal of Clinical Monitoring and Computing, 24(1), 41–50.

Svensson, U. P., Kvaløy, O., & Berg, T. (2015). A comparison of test–retest variability and time efficiency of auditory thresholds measured with pure tone audiometry and new early warning test. Applied Acoustics, 90, 153–159.

Corry, M., Sanders, M., & Searchfield, G. D. (2017). The accuracy and reliability of an app-based audiometer using consumer headphones: pure tone audiometry in a normal hearing group. International Journal of Audiology, 56(9), 706–710.

Margolis, R. H., Glasberg, B. R., Creeke, S., & Moore, B. C. (2010). AMTAS®: Automated method for testing auditory sensitivity: Validation studies. International Journal of Audiology, 49(3), 185–194.

Margolis, R. H., & Moore, B. C. (2011). AMTAS®: Automated method for testing auditory sensitivity: III. Sensorineural hearing loss and air-bone gaps. International Journal of Audiology, 50(7), 440–447.

Margolis, R. H., Frisina, R., & Walton, J. P. (2011). AMTAS®: Automated method for testing auditory sensitivity: II. Air conduction audiograms in children and adults. International Journal of Audiology, 50(7), 434–439.

Sandström, J., Swanepoel, D., Laurent, C., Umefjord, G., & Lundberg, T. (2020). Accuracy and reliability of smartphone self-test audiometry in community clinics in low income settings: a comparative study. Annals of Otology, Rhinology & Laryngology, 129(6), 578–584.

Bornman, M., Swanepoel, D. W., De Jager, L. B., & Eikelboom, R. H. (2019). Extended high-frequency smartphone audiometry: validity and reliability. Journal of the American Academy of Audiology, 30(3), 217–226.

Sandström, J., Swanepoel, D. W., Carel Myburgh, H., & Laurent, C. (2016). Smartphone threshold audiometry in underserved primary health-care contexts. International Journal of Audiology, 55(4), 232–238.

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van Tonder, J., Swanepoel, W., Mahomed-Asmail, F., Myburgh, H., & Eikelboom, R. H. (2017). Automated Smartphone Threshold Audiometry: Validity and Time Efficiency. J Am Acad Audiol, 28(3), 200–208.

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