A practical introduction to machine learning for hearing science
From models of the auditory brain to audio enhancement algorithms and new clinical tools, this tutorial will provide a high-level overview of the ways machine learning is transforming hearing research. Examples will illustrate core concepts in machine learning and emphasize practical considerations for incorporating these methods. The session will consist of a presentation followed by an interactive demonstration with Jupyter notebooks. No programming experience is assumed but basic familiarity with Python or MATLAB will be helpful to get the most out of the hands-on portion. All tutorial materials will be made available on Github.
This workshop will be led by Dr. Mark R. Saddler (Technical University of Denmark). Mark is a postdoc in the Hearing Systems Research Section of DTU’s Department of Health Technology. He works on machine learning models of human hearing, aiming to understand how our ears and environment shape perception. His research computationally links peripheral neural coding and auditory perception to better understand and treat hearing loss. Mark holds a PhD in Computational Neuroscience from MIT.
Organized by VCCA2026