Zolfaghari, Reza, Epain, Nicolas, Jin, Craig et al. (2 more authors) (2017) Kernal principal component analysis of the ear morphology. In: ICASSP 2017, New Orleans, USA. IEEE
Abstract
This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA). Further, it utilizes the framework of large deformation diffeomorphic metric mapping and the vector space that is constructed over the space of initial momentums, which describes the diffeomorphic transformations from the reference template ear shape. The population of ear shapes examined by the KPCA are 124 left and right ear shapes from the SYMARE database that were rigidly aligned to the template (population average) ear. In the work presented here we show the morphological variations captured by the first two kernel principal components, and also show the acoustic transfer functions of the ears which are computed using fast multipole boundary element method simulations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Copyright 2017 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. |
Keywords: | Morphoacoustics,LDDMM,Kernel principal Component analysis,Ear shape analysis,FM-BEM |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 10 Jul 2017 11:00 |
Last Modified: | 02 Apr 2025 23:32 |
Published Version: | https://doi.org/10.1109/ICASSP.2017.7952202 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICASSP.2017.7952202 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118864 |