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Binaural Synthesis Individualization based on Listener Perceptual Feedback

Abstract : In binaural synthesis, providing individual HRTFs (head-related transfer functions) to the end user is a key matter, which is addressed in this thesis. On the one hand, we propose a method that consists in the automatic tuning of the weights of a principal component analysis (PCA) statistical model of the HRTF set based on listener localization performance. After having examined the feasibility of the proposed approach under various settings by means of psycho-acoustic simulations of the listening tests, we test it on 12 listeners. We find that it allows considerable improvement in localization performance over non-individual conditions, up to a performance comparable to that reported in the literature for individual HRTF sets. On the other hand, we investigate an underlying question: the dimensionality reduction of HRTF sets. After having compared the PCA-based dimensionality reduction of 9 contemporary HRTF and PRTF (pinna-related transfer function) databases, we propose a dataset augmentation method that relies on randomly generating 3-D pinna meshes and calculating the corresponding PRTFs by means of the boundary element method.
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Submitted on : Thursday, November 18, 2021 - 12:43:11 PM
Last modification on : Saturday, November 20, 2021 - 3:49:23 AM


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  • HAL Id : tel-03434565, version 1


Corentin Guézénoc. Binaural Synthesis Individualization based on Listener Perceptual Feedback. Signal and Image processing. CentraleSupélec, 2021. English. ⟨NNT : 2021CSUP0004⟩. ⟨tel-03434565⟩



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