Statistical method to identify key anthropometric parameters in hrtf individualization

Zhang, M.; Kennedy, Rodney A.; Abhayapala, Thushara D.; Zhang, W.

doi:10.1109/hscma.2011.5942401

Cited by 26 publications

(13 citation statements)

References 10 publications

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“…The traditional PCA method is generally used in the time or frequency domain of HRTFs [21], [22]. In contrast to traditional PCA models, SPCA is applied to the spatial domain.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Modeling of Individual HRTFs based on Spatial Principal Component Analysis

Zhang,

Ge,

Liu

et al. 2019

Preprint

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Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

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“…The traditional PCA method is generally used in the time or frequency domain of HRTFs [21], [22]. In contrast to traditional PCA models, SPCA is applied to the spatial domain.…”

Section: B Data Preprocessingmentioning

confidence: 99%

Modeling of Individual HRTFs based on Spatial Principal Component Analysis

Zhang,

Ge,

Liu

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Binaural and ambisonics are the two most common recording techniques in soundscape studies. Many have used binaural measurement systems, such as an artificial head or binaural microphones to record acoustic environment and binaural recordings are used for the reproduction of acoustic environments in laboratory-based listening experiments [54][55][56]. Ambisonic recording methods have recently received much attention in soundscape studies, as they are not restricted by playback mediums.…”

Section: Application Of Spatial Audio Recording In Soundscape Studiesmentioning

confidence: 99%

Spatial Audio

2017

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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: 98%

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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Statistical method to identify key anthropometric parameters in hrtf individualization

Cited by 26 publications

References 10 publications

Modeling of Individual HRTFs based on Spatial Principal Component Analysis

Modeling of Individual HRTFs based on Spatial Principal Component Analysis

Spatial Audio

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

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