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

by   Corentin Guezenoc, et al.

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.


page 5

page 6

page 8


Dataset Augmentation and Dimensionality Reduction of Pinna-Related Transfer Functions

Efficient modeling of the inter-individual variations of head-related tr...

Automatic dimensionality selection for principal component analysis models with the ignorance score

Principal component analysis (PCA) is by far the most widespread tool fo...

Differentially private low-dimensional representation of high-dimensional data

Differentially private synthetic data provide a powerful mechanism to en...

Fast Principal Component Analysis for Cryo-EM Images

Principal component analysis (PCA) plays an important role in the analys...

Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes

We consider the problem of matching two shapes assuming these shapes are...

Dynamic Principal Subspaces with Sparsity in High Dimensions

Principal component analysis (PCA) is a versatile tool to reduce the dim...

Please sign up or login with your details

Forgot password? Click here to reset