Computational Infrastructure Archives - Computational Audiology

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

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Post published:June 7, 2021
Post category:Computational Infrastructure Machine Learning for Audiology VCCA2021 ABSTRACTS

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

“Ear in the Clouds”– A web app supporting computational models for auditory-nerve and midbrain responses

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Post published:June 7, 2021
Post category:Computational Infrastructure Computational Models VCCA2021 ABSTRACTS

A cloud-based web app provides an accessible tool for simulation and visualization of population responses of model auditory-nerve and midbrain neurons.

Computational audiology and the technical burden of machine learning

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Post published:June 5, 2020
Post category:Computational Infrastructure Featured talks

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.

Applying Biophysical Auditory Periphery Models for Real-time Applications and Studies of Hearing Impairment

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Post published:June 4, 2020
Post category:Computational Infrastructure Computational Models VCCA2020 ABSTRACTS

Applying biophysical auditory periphery models for real-time applications and studies of hearing impairment

The nine stages of the machine learning workflow.

The critical role of computing infrastructure in computational audiology

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Post published:May 15, 2020
Post category:Big Data in Audiology Computational Infrastructure Machine Learning for 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.

Source: https://www.stripepartners.com/our_writing_article/the-age-of-the-ear/

Computational Audiology: new ways to address the global burden of hearing loss

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Post published:April 6, 2020
Post category:Big Data in Audiology Computational Infrastructure Computational Models e-Audiology Machine Learning for Audiology

Computational audiology, the augmentation of traditional hearing health care by digital methods, has potential to dramatically advance audiological precision and efficiency to address the global burden of hearing loss.