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Head-related transfer functions (HRTFs) individualization is a key matter inbinaural synthesis. However, currently available databases are limited in sizecompared to the high dimensionality of the data. Hereby, we present the processof generating a synthetic dataset of 1000 ear shapes and matching sets ofpinna-related transfer functions (PRTFs), named WiDESPREaD (wide dataset of earshapes and pinna-related transfer functions obtained by random ear drawings)and made freely available to other researchers. Contributions in this articleare three-fold. First, from a proprietary dataset of 119 three-dimensionalleft-ear scans, we build a matching dataset of PRTFs by performingfast-multipole boundary element method (FM-BEM) calculations. Second, weinvestigate the underlying geometry of each type of high-dimensional data usingprincipal component analysis (PCA). We find that this linear machine learningtechnique performs better at modeling and reducing data dimensionality on earshapes than on matching PRTF sets. Third, based on these findings, we devise amethod to generate an arbitrarily large synthetic database of PRTF sets thatrelies 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