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 Q2

Computational Audiology TV and Winners VCCA2021

Research Topic: Digital Hearing Healthcare

The Frontiers Research Topic is now completing the final submissions.

Discussion Group | Fall 2021

Join the CAN discussion group to learn more about AI & Audiology

VCCA2021 Highlights

Here you can find developments in research, innovative projects, software, and tools .

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

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
Brochier et al
A finite element model of a cochlea, a computational model of the auditory nerve, and an automatic speech recognition neural Read more
Picinali et al
Teenagers with bilateral cochlear implants (CI) often suffer from poor spatial hearing abilities; a set of multi-modal (audio-visual) and multi-domain Read more
Migliorini et al
Statistical analysis of how fitting parameters relate to speech recognition scores finds meaningful differences between the highest- and lowest-scoring tertiles Read more
Knoetze et al
In a first of its kind study, we aimed to determine the accuracy and reliability of sound-level monitoring earphones and Read more
Hoogestraat et al
Using a Naive Bayes classifier, we could show that twelve different activities were classified above chance. Read more
Beckers et al
This ongoing study aims to investigate how effortful listening becomes, when neurocognitive mechanisms need to be activated while listening to Read more
Plain et al
Training k-nearest neighbor classifiers to predict intelligibility, social state and participant perception of a listening task Read more
Cheng Leong et al
Formant-frequency difference limens in human with normal hearing or mild sensorineural hearing loss were estimated based on models for neural Read more
This study explores the effects of inhibition and gap junctions on the synchrony enhancement seen in ventral cochlear nucleus bushy Read more

The ideas for this website have been published here
,

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