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…
DALL·E 2023 & Jan-Willem Wasmann, Prompt: audiologist looking at an ear that's also a galaxy, digital art
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?
DALL·E 2023-02-08 09.13.50 - A print in the style of M.C. Escher depicting two man and ChatGPT an AI-chatbot writing a paper collaboratively
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.
Daily LEX8h over time in California, Florida, New York, and Texas.
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.
Illustration of the differences between the clinical test and speech audiometry at home, which can be performed with a smart speaker.
Hearing test using smart speakers: Speech audiometry with Alexa
We present an Alexa skill that performs a speech-in-noise listening test with matrix sentences. The skill is evaluated with four subject groups and in three different acoustic conditions.
The goal is to find the model instance (folder from shelf y) that has the maximum likelihood to have generated the experimental data set d. The stimulus that leads to the smallest variance of parameter estimates is presented next. The process is repeated until the termination criterion is met.
Model-based selection of most informative diagnostic tests and test parameters
The model should conduct the experiment because it knows best which condition is going to be the most informative for confining its free parameters, at least in theory
Detecting hearing loss from children’s speech using machine learning
Here we explore the potential for using machine learning to detect hearing loss from children's speech.
Fig.1 Map of England by Government Office Regions, showing prevalence rates of self-reported hearing loss in eight Waves of the English Longitudinal Study of Ageing (ELSA). This work by Dialechti Tsimpida is licensed under a Creative Commons Attribution 4.0 International License.
Prevalence statistics of hearing loss in adults: Harnessing spatial big data to estimate patterns and trends
Harnessing spatial big data to estimate patterns and trends of hearing loss
Overview of the decision tree to diagnose somatosensory tinnitus
A data-driven decision tree for diagnosing somatosensory tinnitus
Based on the results of an online survey, we developed a decision tree to classify somatosensory tinnitus patients with an accuracy of over 80%.
Examining the association of standard threshold shifts for occupational hearing loss among miners exposed to noise and platinum mine dust at a large-scale platinum mine in South Africa
The association of standard threshold shifts for occupational hearing loss among miners exposed to noise and platinum mine dust at a large-scale platinum mine in South Africa
Evaluation of multivariate classification algorithms for hearing loss detection through a speech-in-noise test
A new online hearing screening procedure integrated with artificial intelligence for the identification of slight/mild hearing loss in older adults.
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.
Clarity Challenge Enhancement Challenge
The Clarity project is running a series of machine learning challenges to revolutionise signal processing in hearing aids.
Digital care pathway
ARO Symposium Forum
This forum is used to facilitate Q&A and provide additional resources at the ARO symposium 'Emerging Capabilities for Evaluating Human Hearing'
ISA webinar about Computational Audiology
Here you can watch the ISA webinar about Computational Audiology held on 9 December 2020 Computational Audiology from…
Final masked AMLAG results for one participant (127) with a left cochlear implant and no residual hearing. Red diamonds denote unheard tones and blue pluses denote heard tones. The most intense tones at lower frequencies in the left ear were effectively masked.
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.
The nine stages of the machine learning workflow.
The critical role of computing infrastructure in computational audiology
The rise of new digital tools for collecting data on scales never before seen in our field coupled with new modeling techniques from deep learning requires us to think about what computational infrastructure we need in order to fully enjoy the benefits and mitigate associated barriers.