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, Associate Professor Philip Leong, Dr 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   

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

Other opportunities with Associate Professor Craig Jin

• Pattern analysis techniques for sound synthesis
• Interpolation of binaural impulse responses for virtual auditory displays
• Sound field recording and recreation
• Beamforming with acoustic vector sensors - Multiple acoustic source localisation using acoustic vector sensor arrays
• Speech separation and localisation using particle filtering
• 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
• Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
• Impedance tomography for cardiac imaging: high speed tomography
• Medical diagnostics for neonates in the developing world
• FPGA-based Low Latency Trading
• Floating Point FPGA Architectures
• Placement-aware Hardware Description Languages
• Scalable vision machines
• Modelling Parkinson's disease using control models
• Novel Electrodes for rapid electrophysiological recording

Other opportunities with Associate Professor Philip Leong

• other research opportunities available at Faculty of Engineering and Information Technologies
• FPGA-based Low Latency Trading
• Floating Point FPGA Architectures
• Placement-aware Hardware Description Languages
• Scalable vision machines
• Modelling Parkinson's disease using control models
• Mapping 2D Images to 3D Shape
• New technique for studying human brain activity
• Next Generation Audio Coding
• Spherical multi-modal scene analysis
• Medical diagnostics for neonates in the developing world
• Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
• Impedance tomography for cardiac imaging: high speed tomography
• Novel Electrodes for rapid electrophysiological recording
• Binaural signal processing algorithms for hearing aids

Other opportunities with Dr Alistair McEwan

• Medical diagnostics for neonates in the developing world
• Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
• Impedance tomography for cardiac imaging: high speed tomography
• Novel Electrodes for rapid electrophysiological recording
• Mapping 2D Images to 3D Shape
• New technique for studying human brain activity
• Next Generation Audio Coding
• Spherical multi-modal scene analysis
• FPGA-based Low Latency Trading
• Floating Point FPGA Architectures
• Placement-aware Hardware Description Languages
• Scalable vision machines
• Modelling Parkinson's disease using control models
• Binaural signal processing algorithms for hearing aids