Statistical models of ear shape and ear acoustics


The future of personal audio devices relies on statistical shape analysis of ears and ear acoustics. We have the world’s largest database of head-ear meshes together with ear acoustic data. The task is to explore statistical models of these data and apply leading machine learning techniques to map the data.


Associate Professor Craig Jin, Professor Philip Leong, Professor Alistair McEwan

Research Location

Electrical and Information Engineering

Program Type



This leading research project explores advanced mathematical techniques for statistical shape analysis of ears and ear acoustics. Advanced machine learning techniques will be applied to map shape data to acoustic data. Topics include pattern theory and generative statistical models. We will work with leading international research teams.

Additional Information

Successful candidates likely have a background in mathematical modeling and machine learning.

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Statistical shape analysis, Machine learning, morphable models, head-related transfer functions, Spatial Audio, ear shape, 3D shape from 2D images, 3D

Opportunity ID

The opportunity ID for this research opportunity is: 1359

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