A multiscale LDDMM template algorithm for studying ear shape variations

Abstract

This paper describes a method to establish an average human ear shape across a population of ears by sequentially 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.