Statistical models of ear shape and ear acoustics
Summary
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.
Supervisor(s)
Associate Professor Craig Jin, Professor Philip Leong, Professor Alistair McEwan
Research Location
Electrical and Information Engineering
Program Type
Masters/PHD
Synopsis
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.
http://www.ee.usyd.edu.au/carlab
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Keywords
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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Other opportunities with Professor Alistair McEwan
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- Mapping 2D Images to 3D Shape
- New technique for studying human brain activity
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Binaural signal processing algorithms for hearing aids
- FPGA-based low latency machine learning