Considerations Regarding Individualization of Head-Related Transfer Functions

Jin, Craig; Zolfaghari, Reza; Long, Xian; Sebastian, Arun; Hossain, Shafayet Shariar; Glaunès, Joan Alexis; Tew, Anthony I.; Shahnawaz, Muhammad; Sarti, Augusto

doi:10.1109/icassp.2018.8462613

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…In order to illustrate these issues we consider some recent data [43] for an experiment contrasting individualized HRIRs and generic HRIRs. This description is taken from [43].…”

Section: Head-tracking and Individualizationmentioning

confidence: 99%

“…In order to illustrate these issues we consider some recent data [43] for an experiment contrasting individualized HRIRs and generic HRIRs. This description is taken from [43]. There were four listening conditions: (1) binaural rendering with individualized HRIR filters and head-tracking; (2) binaural rendering with generic HRIR filters and head-tracking; (3) binaural rendering with individualized HRIR filters and no head-tracking; and (4) normal headphone listening without binaural spatial rendering.…”

Section: Head-tracking and Individualizationmentioning

confidence: 99%

“…no HT À Individualized HRIRs with no head-tracking; No 3D À no binaural spatial rendering. Figures taken from [43].…”

Section: Manipulation Of the Covariance Matrixmentioning

confidence: 99%

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A tutorial on immersive three-dimensional sound technologies

Jin

2020

Acoust. Sci. & Tech.

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There is renewed interest in virtual auditory perception and spatial audio arising from a technological drive toward enhanced perception via mixed-reality systems. Because the various technologies for three-dimensional (3D) sound are so numerous, this tutorial focuses on underlying principles. We consider the rendering of virtual auditory space via both loudspeakers and binaural headphones. We also consider the recording of sound fields and the simulation of virtual auditory space. Special attention is given to areas with the potential for further research and development. We highlight some of the more recent technologies and provide references so that participants can explore issues in more detail.

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“…In order to illustrate these issues we consider some recent data [43] for an experiment contrasting individualized HRIRs and generic HRIRs. This description is taken from [43].…”

Section: Head-tracking and Individualizationmentioning

confidence: 99%

Section: Head-tracking and Individualizationmentioning

confidence: 99%

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A tutorial on immersive three-dimensional sound technologies

Jin

2020

Acoust. Sci. & Tech.

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“…So, it is possible to select best-matching HRTFs from an HRTF baseline database according to the deviation in pinnae parameters. Then, subsequent studies on the optimization of matching parameters were carried out [13][14][15][16][17][18][19][20][21]. Zeng et al [13] explored the statistical relationships between HRTF characteristic vectors and anthropometric parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In the method, the optimal frequency scale factor inferred from the anthropometric parameters was applied to the known HRTFs to obtain the approximation of the individualized HRTFs. Generally, the current mainstream method is to use correlation analysis and multiple linear regression analysis to find anthropometric parameter sets that are closely related to HRTFs [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm for Predicting Median-Plane Head-Related Transfer Functions from Anthropometric Measurements

2019

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Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front-back and up-down confusion and improve the accuracy of localization for most subjects.

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