Featured talks for the VCCA2020 conference
Celebrating a milestone of the Computational Audiology Network
Written by Jan-Willem Wasmann, using Jenni AI and Grammarly. - I couldn't find a way to create a more modest tone ;-). Afterwards, I ran it typing Mind (that uses…
AI’s Latest Frontier part 2: AI chatbots and clinical audiology
How might people with hearing loss (patients) or clinicians (Audiologist and ENT-specialist) in hearing healthcare use ChatGPT or other AI Chatbots?
AI’s Latest Frontier: Transforming Hearing Healthcare
We used ChatGPT a AI system that can help writing text and code or answer questions but that cannot take responsibility in a way a human writer can do.
Quarterly update Q6-Q8
In this edition, we will be covering a variety of topics, including the Peer Recommender Challenge.
Real Meetings: Virtual Versus In-Person
December 17 Written and edited by Jan-Willem Wasmann with contributions from Seba Ausili, Liepollo Ntlhakana, Bill Whitmer, Soner Türüdü, Rob Eikelboom, Elle O’Brien, Deniz Başkent, Dennis Barbour, Dave Moore &…
VCCA2022 Conference Report
With five keynote talks, four special sessions, and over 500 registered participants, the VCCA keeps growing.
Podcast Episode 3: A holistic perspective on hearing technology
n this episode, Brent Edwards from NAL and Stefan Launer from Sonova take us through their careers and share lessons and perspectives on the development of hearing technology. We discuss how the development of technology becomes more holistic, design thinking, standardization, and what's needed to get to new service models and innovation.
Podcast Episode 2: Automated Speech Recognition (ASR) for the deaf
Automated Speech Recognition (ASR) for the deaf and communication on equal terms regardless of hearing status. Episode 2 with Dimitri Kanevsky, Jessica Monaghan and Nicky Chong-White. Moderator Jan-Willem Wasmann You…
Podcast Episode 1: Bayesian Active Learning in Audiology
Active Learning in the auditory domain A round table with Bert de Vries, Josef Schlittenlacher and Dennis Barbour. Moderator Jan-Willem Wasmann Audio version: Audiovisual version on Youtube: Josef Schlittenlacher is…
The Global Burden of Hearing Loss and Future Hearing Aids
This post covers the VCCA discussions on hearing devices of the future, and hearing diagnostics and services of the future. Both specifically discussed solutions and ideas for low-income countries.
Twenty-five years of clinical data collection: from a single site relational database towards multi-site interoperability
Over the years Hannover Medical School has build a comprehensive data pool for patients with implantable hearing devices, which serves as a basis for answering various research questions and big data analyses.
Towards Smarter and Connected Hearing Implant Care
Connectivity is transforming the hearing care model significantly.
The HiGHmed approach for FAIR use of clinical and research data with openEHR – Focusing on interoperability
Patient-centric medical research benefits from the sharing and integration of complex and diverse data from different sources such as care, clinical research, and novel emerging data types.
We have lots of data – now what?
There is no doubt that the move away from paper-based health records to electronic health records has been important for many reasons.
Clarity: machine learning challenges for improving hearing aid processing of speech in noise
In the Clarity project, the machine learning approach is applied to the problem of hearing aid processing of speech-in-noise.
Machine learning for models of auditory perception
Machine learning has various applications in auditory modelling and the approaches combined will transform individual testing and processing in hearing devices.
Learning from audiological data collected in the lab and the real world
Research in the last decades with the audiological data led to many important discoveries, and today, as the area of data emerges the focus turns to maturing those discoveries along the dimensions of coverage, applicability, bias, and privacy into solutions that improve the lives for people with hearing problems.
Hearing deficits and auditory profiling: data-driven approaches towards personalized audiology
herethe concept and principles of knowledge discovery from databases are revisited and applied on the scope of two recently published studies about auditory profiling.
Virtual audiovisual environments for hearing aid evaluation (and fitting)
In our group at Oldenburg University, we developed virtual audiovisual environments representing everyday-life listening situations.
Big Data and the Apple Hearing Study
The University of Michigan School of Public Health has partnered with Apple Inc. to use advances in smart device and wearable technology to evaluate the levels of sound at which iPhone users listen to music and other media, as well as how long and how often they listen.
Can AI-led hearing healthcare address the growing global burden of hearing loss?
According to the recent WHO World Report on Hearing, there are approximately 500 million people worldwide with disabling hearing loss, the vast majority of whom receive no treatment. The consequences of this unmet need are dire: hearing loss is a top-5 contributor to the global burden of disability; the leading modifiable risk factor for dementia; and costs nearly 1 trillion dollars per year.
ISA webinar about Computational Audiology
Here you can watch the ISA webinar about Computational Audiology held on 9 December 2020 Computational Audiology from…
Entering the Era of Global Tele-Audiology
The COVID-19 crisis has rapidly and dramatically expanded the global interest in and demand for tele-audiology services, even in highly developed countries with well-established audiological services.
Improving music enjoyment and speech-in-speech perception in cochlear implant users: a planned piano lesson intervention with a serious gaming control intervention
Although cochlear implants (CIs) provide hearing for severely-hearing-impaired or deaf populations, certain hearing abilities remain challenging for CI users.
The role of computational auditory models in auditory precision diagnostics and treatment
Auditory modeling is indispensable for precision diagnostics and individualized treatment
Computational audiology and the technical burden of machine learning
The biggest hurdle facing computational audiologists may not be perfecting machine learning methods, but creating the infrastructure- including hardware, data management tools, and software engineering skills- to support this new type of research.
Dynamically Masked Audiograms with Machine Learning Audiometry
Dynamically masked audiograms achieve accurate true threshold estimates and reduce test time compared to current clinical masking procedures.