The computational infrastructure and software needed to implement 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.
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
Continue ReadingModel-based selection of most informative diagnostic tests and test parameters
A cloud-based web app provides an accessible tool for simulation and visualization of population responses of model auditory-nerve and midbrain neurons.
Continue Reading“Ear in the Clouds”– A web app supporting computational models for auditory-nerve and midbrain responses
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.
Continue ReadingComputational audiology and the technical burden of machine learning
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.
Continue ReadingThe critical role of computing infrastructure in computational 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.
Continue ReadingComputational Audiology: new ways to address the global burden of hearing loss
