PROJECT SUMMARY / ABSTRACT This work provides fundamental knowledge for the development of wideband acoustic immittance (WAI) measures as a noninvasive auditory diagnostic tool, where WAI refers to the collection of measurements that include absorbance, power reflectance, impedance, and related quantities. Potential uses of these measures include (1) detection of fluid in newborn and young infant ears where tympanometry is less successful and early diagnosis can be critical to supporting normal speech and language development, (2) identification of the cause of a conductive hearing loss (e.g., fluid, disarticulated ossicle, fixed ossicle), and (3) monitoring changes in middle-ear stiffness that result from intracranial pressure changes. The proposed work continues to expand and update the world’s only online WAI database and corresponding website, which was developed during the first two cycles of this grant; the database now includes more than 4.9 million rows of WAI data from 9903 subjects and 41 publications that collectively include both normal and diseased ears from subjects of all ages. This database has provided and will continue to provide additional measurements that the hearing community can utilize to (1) define features of normative measures for clinical application of WAI measurements, (2) determine how specific pathologies affect WAI, and (3) train systems that apply machine learning for the interpretation of WAI measurements. The proposed work expands upon the work in previous grant cycles to include (1) controlled laboratory-based WAI measurements that will inform criteria for the development of WAI measurement validity criteria by systematically studying acoustic leaks and probe placements along the canal and (2) the development of machine-learning models to classify WAI measurements made on normal and abnormal ears and to ultimately classify specific types of pathologies. A second emphasis of the proposed work is the research-based education of undergraduate students at Smith College, an all-women’s liberal arts college. Undergraduate engineering, computer science, statistics, data science, and mathematics students will be actively involved in all areas of the proposed work, with the goal of encouraging them to continue their education at the graduate level where they can contribute to health-related research.