Improved method to individualize head-related transfer function using anthropometric measurements

Xu, Song; Li, Zhizhong; Salvendy, Gavriel

doi:10.1250/ast.29.388

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…Most work in the literature either stacks the HRTFs of various directions and subjects into a data matrix of size (n • n d ) × n f prior to PCA [21,28], or performs PCA one direction at a time on n d different n × n fsized matrices [31,39]. In contrast, we chose to concatenate PRTFs from the n d directions into a row vector q i ∈ R n f n d for each subject i = 1, .…”

Section: Pca Of Log-magnitude Prtfsmentioning

confidence: 99%

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

Guezenoc,

Seguier

2020

Preprint

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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.

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Section: Pca Of Log-magnitude Prtfsmentioning

confidence: 99%

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

Guezenoc,

Seguier

2020

Preprint

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“…One such method uses principal component analysis (PCA), which resolves an HRTF into its principal components (Kistler and Wightman, 1992;Middlebrooks and Green, 1992). The coefficients of each principal component are then estimated based on the anthropometry of the listener's pinnae (Hu et al, 2008;Xu et al, 2008;Hugeng and Gunawan, 2010;Zhang et al, 2011). However, the results of sound localization tests were not reported in these studies.…”

Section: Introductionmentioning

confidence: 99%

Personalization of head-related transfer functions in the median plane based on the anthropometry of the listener's pinnae

Iida

Ishii

Shinsuke

2014

The Journal of the Acoustical Society of America

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A listener's own head-related transfer functions (HRTFs) are required for accurate three-dimensional sound image control. The HRTFs of other listeners often cause front-back confusion and errors in the perception of vertical angles. However, measuring the HRTFs of all listeners for all directions of a sound source is impractical because the measurement requires a special apparatus and a lot of time. The present study proposes a method for estimating the appropriate HRTFs for an individual listener. The proposed method estimates the frequencies of the two lowest spectral notches (N1 and N2), which play an important role in vertical localization, in the HRTF of an individual listener by anthropometry of the listener's pinnae. The best-matching HRTFs, of which N1 and N2 are the closest to the estimates, are then selected from an HRTF database. In order to examine the validity of the proposed method, localization tests in the upper median plane were performed using four subjects. The results revealed that the best-matching HRTFs provided approximately the same performance as the listener's own HRTFs for the target directions of the front and rear for all four subjects. For the upper target directions, however, the performance of the localization for some of the subjects decreased.

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“…Due to the high dimensionality, it is necessary to extract the individual factors with lower dimension from the original HRTFs and get rid of non-individual features. Principal component analysis (PCA) was popularly applied to get individual weight coefficients and basis vectors before the HRTF customization [11][12][13][14][15]. Sodnik et al found a suitable representation for the weight variations of the HRTF amplitudes by PCA [16].…”

Section: Introductionmentioning

confidence: 99%

“…The anthropometric parameters were treated as the inputs and lower-dimensional HRTFs as the outputs. Many researchers constructed the HRTF prediction model based on an assumption of a linear relation between the HRTF vectors and anthropometric parameters [11][12][13]24,28,29]. In [12,24,28,29], the relation between the HRTFs and physical sizes of the head and ear was investigated by the multiple regression analysis and optimized by the least squares method.…”

Section: Introductionmentioning

confidence: 99%

Modeling individual HRTF tensor using high-order partial least squares

Huang

2014

EURASIP J. Adv. Signal Process.

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A tensor is used to describe head-related transfer functions (HRTFs) depending on frequencies, sound directions, and anthropometric parameters. It keeps the multi-dimensional structure of measured HRTFs. To construct a multi-linear HRTF personalization model, an individual core tensor is extracted from the original HRTFs using high-order singular value decomposition (HOSVD). The individual core tensor in lower-dimensional space acts as the output of the multi-linear model. Some key anthropometric parameters as the inputs of the model are selected by Laplacian scores and correlation analyses between all the measured parameters and the individual core tensor. Then, the multi-linear regression model is constructed by high-order partial least squares (HOPLS), aiming to seek a joint subspace approximation for both the selected parameters and the individual core tensor. The numbers of latent variables and loadings are used to control the complexity of the model and prevent overfitting feasibly. Compared with the partial least squares regression (PLSR) method, objective simulations demonstrate the better performance for predicting individual HRTFs especially for the sound directions ipsilateral to the concerned ear. The subjective listening tests show that the predicted individual HRTFs are approximate to the measured HRTFs for the sound localization.

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Improved method to individualize head-related transfer function using anthropometric measurements

Cited by 7 publications

References 12 publications

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

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

Personalization of head-related transfer functions in the median plane based on the anthropometry of the listener's pinnae

Modeling individual HRTF tensor using high-order partial least squares

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