TITLE: A Wide Dataset of Ear Shapes and Pinna-Related Transfer Functions Generated by Random Ear Drawings 

ABSTRACT: Head-related transfer functions (HRTFs) individualization is a key matter in
binaural synthesis. However, currently available databases are limited in size
compared to the high dimensionality of the data. Hereby, we present the process
of generating a synthetic dataset of 1000 ear shapes and matching sets of
pinna-related transfer functions (PRTFs), named WiDESPREaD (wide dataset of ear
shapes and pinna-related transfer functions obtained by random ear drawings)
and made freely available to other researchers. Contributions in this article
are three-fold. First, from a proprietary dataset of 119 three-dimensional
left-ear scans, we build a matching dataset of PRTFs by performing
fast-multipole boundary element method (FM-BEM) calculations. Second, we
investigate the underlying geometry of each type of high-dimensional data using
principal component analysis (PCA). We find that this linear machine learning
technique performs better at modeling and reducing data dimensionality on ear
shapes than on matching PRTF sets. Third, based on these findings, we devise a
method to generate an arbitrarily large synthetic database of PRTF sets that
relies on the random drawing of ear shapes and subsequent FM-BEM computations.
Head-related transfer functions (HRTFs) individualization is a key matter in
binaural synthesis. However, currently available databases are limited in size
compared to the high dimensionality of the data. Hereby, we present the process
of generating a synthetic dataset of 1000 ear shapes and matching sets of
pinna-related transfer functions (PRTFs), named WiDESPREaD (wide dataset of ear
shapes and pinna-related transfer functions obtained by random ear drawings)
and made freely available to other researchers. Contributions in this article
are three-fold. First, from a proprietary dataset of 119 three-dimensional
left-ear scans, we build a matching dataset of PRTFs by performing
fast-multipole boundary element method (FM-BEM) calculations. Second, we
investigate the underlying geometry of each type of high-dimensional data using
principal component analysis (PCA). We find that this linear machine learning
technique performs better at modeling and reducing data dimensionality on ear
shapes than on matching PRTF sets. Third, based on these findings, we devise a
method to generate an arbitrarily large synthetic database of PRTF sets that
relies on the random drawing of ear shapes and subsequent FM-BEM computations.
A Wide Dataset of Ear Shapes and Pinna-Related Transfer Functions Generated by Random Ear Drawings
http://arxiv.org/abs/2003.06182v1