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 and funding opportunities 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 or check out videos on Computational Audiology TV.

News & Agenda

Quarterly Update Q1

Call for abstracts VCCA2021 is open! Winner Computational audiology prize announced, and check out recent publications.

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 Highlights

At the Virtual Conference on Computational Audiology 2021 (VCCA 2021) you can follow new developments related to research, innovative projects, software, tools and other applications related to Computational Audiology.

VCCA2021 Abstracts

All abstracts are published on the forum. You can find them here.

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

In our group at Oldenburg University, we developed virtual audiovisual environments representing everyday-life listening situations. Read more
The University of Michigan School of Public Health has partnered with Apple Inc. to use advances in smart device and Read more
Ooster et al
We present an Alexa skill that performs a speech-in-noise listening test with matrix sentences. The skill is evaluated with four Read more
Herrmann et al
The model should conduct the experiment because it knows best which condition is going to be the most informative for Read more
Here we explore the potential for using machine learning to detect hearing loss from children's speech. Read more
Dalia et al
Harnessing spatial big data to estimate patterns and trends of hearing loss Read more
Keshavarzi et al
Transient noise reduction using a deep recurrent neural network improves the subjective speech Intelligibility and comfort. Read more
Rossbach et al
In this study, we show that phoneme probabilities from a DNN can produce good estimates of speech intelligibility when combined Read more
Huelsmeier et al
Attenuation-component-based model predictions of speech recognition thresholds like FADE seem to facilitate an estimation of the supra-threshold distortion component of Read more
Saddler et al
We developed a deep learning model of hearing loss by training artificial neural networks to recognize words in noise from 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