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. Here is an overview of services for professionals. In addition, the website is used to promote events related to computational audiology and as a central hub to share resources and funding opportunities, and job opportunities for researchers and clinicians. For clinicians, we added this overview of automated audiometry approaches.

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. Or join our Slack group.

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

Read our publication on computational audiology or check out videos on Computational Audiology TV.

News & Agenda

Episode 1 Bayesian Active Learning in Audiology

Wikipedia

Wikipedia entry about Computational Audiology

Quarterly Update Q5

recent publications and podcast launch

Research Topic: Digital Hearing Healthcare

The Frontiers Research Topic is now completing the final submissions.

VCCA2022 Highlights

The program is taking shape. Check-out the highlights.

Computational Audiology Network Slack Channel

Interested in machine learning & big data for audiology, hearing tech, or auditory neuroscience? We started a Slack channel for folks involved in: - basic science & translational research - clinical practice - industry - start ups - public health - health policy and advocacy

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 VCCA2021 conference and repositories published in the Zenodo community for computational audiology. Check out the program highlights for the upcoming VCCA2021 (25 June 2021). All abstracts for VCCA2021 are published on the forum. An overview of the VCCA2020 conference program is provided here. Further backgrounds can be found in the virtual goodiebag.  

More resources and demonstrations

Blogs and conference proceedings

n this episode, Brent Edwards from NAL and Stefan Launer from Sonova take us through their careers and share lessons Read more
Here you find the latest news and developments in computational audiology Read more
Automated Speech Recognition (ASR) for the deaf and communication on equal terms regardless of hearing status. Episode 2 with Dimitri Read more
Active Learning in the auditory domain A round table with Bert de Vries, Josef Schlittenlacher and Dennis Barbour. Moderator Jan-Willem Read more
The University of Michigan School of Public Health has partnered with Apple Inc. to use advances in smart device and Read more
Here we explore the potential for using machine learning to detect hearing loss from children's speech. 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