Dataset Augmentation and Dimensionality Reduction of Pinna-Related Transfer Functions
Augmentation de données et réduction de dimensionnalité de fonctions de transfert relatives à l'oreille (PRTFs)
Résumé
Efficient modeling of the inter-individual variations of head-related transfer functions (HRTFs) is a key matter
to the individualization of binaural synthesis. In previous work, we augmented a dataset of 119 pairs of ear
shapes and pinna-related transfer functions (PRTFs), thus creating a wide dataset of 1005 ear shapes and PRTFs
generated by random ear drawings (WiDESPREaD) and acoustical simulations. In this article, we investigate the
dimensionality reduction capacity of two principal component analysis (PCA) models of magnitude PRTFs, trained
on WiDESPREaD and on the original dataset, respectively. We find that the model trained on the WiDESPREaD
dataset performs best, regardless of the number of retained principal components.
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