A multiscale LDDMM template algorithm for studying ear shape variations

TY - CONF

T1 - A multiscale LDDMM template algorithm for studying ear shape variations

AU - Zolfaghari, Reza

AU - Epain, Nicolas

AU - Jin, Craig

AU - Tew, Anthony I

AU - Glaunès, Joan

N1 - INSPEC accession number 14881796

PY - 2014/12

Y1 - 2014/12

N2 - This paper describes a method to establish an average human ear shape across a population of ears by se-quentially applying the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework at successively smaller physical scales. Determining such a population average ear shape, also referred to here as a template ear, is an essential step in studying the statistics of ear shapes because it allows the variations in ears to be studied relative to a common template shape. Our interest in the statistics of ear shapes stems from our desire to understand the relationship between ear morphology and the head-related impulse response (HRIR) filters that are essential for rendering 3D audio over headphones. The shape of the ear varies among listeners and is as individualized as a fingerprint. Because the acoustic filtering properties of the ears depend on their shape, the HRIR filters required for rendering 3D audio are also individualized. The contribution of this work is the demonstration of a sequential multiscale approach to creating a population template ear shape using the LDDMM framework. In particular we apply our sequential multiscale algorithm to a small population of synthetic ears in order to analyse its performance given a known reference ear shape.

AB - This paper describes a method to establish an average human ear shape across a population of ears by se-quentially applying the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework at successively smaller physical scales. Determining such a population average ear shape, also referred to here as a template ear, is an essential step in studying the statistics of ear shapes because it allows the variations in ears to be studied relative to a common template shape. Our interest in the statistics of ear shapes stems from our desire to understand the relationship between ear morphology and the head-related impulse response (HRIR) filters that are essential for rendering 3D audio over headphones. The shape of the ear varies among listeners and is as individualized as a fingerprint. Because the acoustic filtering properties of the ears depend on their shape, the HRIR filters required for rendering 3D audio are also individualized. The contribution of this work is the demonstration of a sequential multiscale approach to creating a population template ear shape using the LDDMM framework. In particular we apply our sequential multiscale algorithm to a small population of synthetic ears in order to analyse its performance given a known reference ear shape.

KW - ear shape

KW - morphology

KW - head-related transfer function

KW - SYMARE

U2 - 10.1109/ICSPCS.2014.7021100

DO - 10.1109/ICSPCS.2014.7021100

M3 - Paper

T2 - IEEE 8th International Conference on Signal Processing and Communication Systems (ICSPCS), 2014, , QLD

Y2 - 15 December 2014 through 17 December 2014

ER -