In audiology, there is growing interest in new approaches to research and clinical practice, such as large-scale data collection, remote audiological services, and machine learning tools. Validating these approaches will be only the first step for computational audiologists, though: systems research indicates that in industrial teams using machine learning and big data, developing statistical models is frequently rated as easier than managing machine learning projects. This talk will explain how emerging methods in computational audiology pose new challenges for labs and research centers that have not yet been seen in traditional psychoacoustics, including hardware, data sharing, and software engineering requirements. It will also overview systemic barriers that the audiology community will need to address to enable rigorous, open science at new scales.
