Why Computational Audiology ?

The purpose of this online forum is to share knowledge and tools related to 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 related to computational audiology and as a central hub to share resources that are useful for researchers and clinicians.

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.

What is computational audiology?

Computational audiology is the augmentation of traditional hearing health care by digital methods including artificial intelligence and machine learning. Continue reading

Let’s democratize audiology for persons suffering from hearing loss worldwide. Promote FAIR data policies and Open Science initiatives in audiology for researchers and clinicians. Create methods to deal with the layers of complexity and enhance interoperability. If we work together with all stakeholders involved collaborate on global standards, on a computational infrastructure, and on policies to increase the transparency and responsible use of AI in audiology we can reduce the global burden of hearing loss.

Join the Computational audiology LinkedIn group via this link.

Add your repository to the  Zenodo community for computational audiology via this link.

Read our publication on computational audiology

News & Agenda

Donders Institute and Radboud University open of a third ICAI lab.

The AI for Neurotech Lab aims to develop machine learning solutions for brain reading and writing, to restore sensory and cognitive functions. These solutions could include hearing tools for the deaf ...

Research Topic: Digital Hearing Healthcare

The Frontiers Research Topic “Digital Hearing Healthcare,” edited by Qinglin Meng, Jing Chen, Changxin Zhang, Dennis Barbour and Fan-Gang Zeng, is now open for submissions.

VCCA2021 Call for Abstracts

The Virtual Conference on Computational Audiology 2021 (VCCA 2021) will be soon accepting abstracts on research, innovative projects, software, tools and other applications related to Computational Audiology.

Blogs and project about computational audiology

Below you find blogs about computational audiology, but also featured talks, and presentations of on-going projects submitted for the VCCA2020 conference and repositories published in the Zenodo community for computational audiology. An overview of the VCCA2020 conference program is provided here. Further backgrounds can be the virtual goodiebag.  

Zenodo Repositories:


7 documents
  • tobiasherzke, Paul Maanen, frasherloshaj, hendrikkayser, Marc Joliet, genckamil, steffendasenbrock, Giso Grimm, Muhammad Zain Sohail. (March, 2021). HoerTech-gGmbH/openMHA: Release 4.15.0 (Version v4.15.0). Zenodo. https://doi.org/10.5281/zenodo.4650058
  • Zinner, Christina, Winkler, Alexandra, Holube, Inga. (March, 2021). Speech Adjusted Noises (SAN) for German speech recognition tests. Zenodo. https://doi.org/10.5281/zenodo.4609783
  • Hendrik Kayser, Tobias Herzke, Paul Maanen, Max Zimmermann, Giso Grimm, Volker Hohmann. (March, 2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). Zenodo. https://doi.org/10.5281/zenodo.4601604
  • Raul Sanchez-Lopez, Fereczkowski, Michal, Santurette, Sébastien, Dau, Torsten, Neher, Tobias. (April, 2020). Data and materials from: “Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation” (Version 1.1). Zenodo. https://doi.org/10.5281/zenodo.4421553
  • Sanchez-Lopez, Raul, Nielsen, Silje Grini, El-Haj-Ali, Mouhamad, Bianchi, Federica, Fereczkowski, Michal, Cañete, Oscar, Wu, Mengfan, Neher, Tobias, Dau, Torsten, Sébastien Santurette. (December, 2019). Data from “Auditory tests for characterizing hearing deficits: The BEAR test battery” (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.3459580
  • Hohmann, Volker, Herzke, Tobias. (February, 2007). Software for “Frequency analysis and synthesis using a Gammatone filterbank” (Version 1.1). Zenodo. https://doi.org/10.5281/zenodo.2643400
  • Josupeit, Angela, Schoenmaker, Esther, van de Par, Steven, Hohmann, Volker. (May, 2018). Raw data for “Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task”. Zenodo. https://doi.org/10.5281/zenodo.1246836

More resources and demonstrations

Blogs and conference proceedings

The U.S. National Hearing Test has now been taken by over 150,000 people and this extensive database provides reliable estimates Read more
Detection of current shunts with a ladder-network model Read more
Test-retest analysis of aggregated audiometry testing data using Jacoti Hearing Center self-testing application Read more
Towards the development of a diagnostic supporting tool in audiology, the Common Audiological Functional Parameters (CAFPAs) were shown to be Read more
Applying biophysical auditory periphery models for real-time applications and studies of hearing impairment Read more
This study used machine learning to identify normal-hearing listeners with and without tinnitus based on their ABRs. Read more
A system that predicts and identifies neural responses to overlapping speech sounds mimics human perception. Read more
Diotic and antiphasic digits-in-noise to detect and classify types of hearing loss Read more
A simple at-home self-check to screen for aberrant loudness growth in hearing aid and cochlear implant users Read more

The ideas for this website have been published here
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Bibliography:

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