Efficient Representation of Head-Related Transfer Functions With Combination of Spherical Harmonics and Spherical Wavelets

Liu, Huaping; Fang, Yong; Huang, Qinghua

doi:10.1109/access.2019.2921388

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…The 2D interpolation using a paraboloid surface fitted in the ITD data in the vicinity of the maximum ITD offers reasonable estimation of the values directions of the maximum ITD and allows comparisons between data sets. Some authors have used spherical harmonic model fits to HRTF data (see, for example, [11,43]), and spherical harmonics could, in principle, fit to the complete 3D ITD profile, but these methods easily become computationally heavy without offering significant benefits over local fitting close to the maximum ITD value for the type of problem being worked with.…”

Section: Discussionmentioning

confidence: 99%

Interaural Time Difference Prediction Using Anthropometric Interaural Distance

Johansson¹,

Mäkivirta²,

Malinen³

et al. 2022

J. Audio Eng. Soc.

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This paper studies the feasibility of predicting the interaural time difference (ITD) in azimuth and elevation once the personal anthropometric interaural distance is known, proposing an enhancement for spherical head ITD models to increase their accuracy. The method and enhancement are developed using data in a Head-Related Impulse Response (HRIR) data set comprising photogrammetrically obtained personal 3D geometries for 170 persons and then evaluated using three acoustically measured HRIR data sets containing 119 persons in total.The directions include 360 • in azimuth and -15 • to 60 • in elevation. The prediction error for each data set is described, the proportion of persons under a given error in all studied directions is shown, and the directions in which large errors occur are analyzed. The enhanced spherical head model can predict the ITD such that the first and 99th percentile levels of the ITD prediction error for all persons and in all directions remains below 122 μs. The anthropometric interaural distance could potentially be measured directly on a person, enabling personalized ITD without measuring the HRIR. The enhanced model can personalize ITD in binaural rendering for headphone reproduction in games and immersive audio applications.

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Section: Discussionmentioning

confidence: 99%

Interaural Time Difference Prediction Using Anthropometric Interaural Distance

Johansson¹,

Mäkivirta²,

Malinen³

et al. 2022

J. Audio Eng. Soc.

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“…(a) Spherical harmonics

Y_{ℓ}^{m} (θ, ϕ)

of different degree

l

with order

m

from

- l

l

are shown with image from @2021 IEEE. Reprinted, with permission, from Liu et al (2019). (b) The radial polynomials for order n = 0, 1, 2, and 3 are illustrated…”

Section: Methodsmentioning

confidence: 99%

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Zhu

Gwo

Deng

et al. 2022

Human Brain Mapping

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Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. Mild cognitive impairment (MCI) is considered a transitional phase between healthy cognitive aging and dementia. Progressive brain volume reduction/atrophy, particularly of the hippocampus, is associated with the transition from normal to MCI, and then to AD. We aimed to develop methods to characterize the shape of hippocampus and explore its potential as an imaging marker to monitor clinical AD progression. We implemented a 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high‐quality T 1 ‐weighted volumetric brain scans from the Alzheimer's Disease Neuroimaging Initiative data set (151 normal, 258 MCI, and 19 AD). Over 2 years, 15 cognitively normal subjects converted to MCI, and 42 subjects with MCI converted to AD. We found a significant correlation between hippocampal volume changes and Zernike shape metrics. Before a clinical diagnosis of AD, the shapes of the left and right hippocampi changed slowly. After AD diagnosis, both volume and shape changed rapidly but were uncorrelated to each other. During the transition from a clinical diagnosis of MCI to AD, the shape of the left and right hippocampi changed in a correlated manner but became uncorrelated after AD diagnosis. Finally, the pace of hippocampus shape change was associated with its shape and the subject's age and disease condition. In conclusion, the hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.

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“…where R is the rotation matrix in (6). Spectral coefficients of the rotated signal are given by [34]…”

Section: B Signal Rotation On the Spherementioning

confidence: 99%

“…Spherical signal processing is the study and analysis of spherical signals, i.e., signals defined on the sphere, which are naturally encountered in many areas of science and engineering such as computer graphics [1], medical imaging [2]- [4], acoustics [5], [6], planetary sciences [7]- [11], geophysics [12], [13], cosmology [14]- [16], quantum mechanics [17], wireless communications [18]- [20] and antenna design [21], to name a few. A natural choice of basis functions for the representation of signals on the sphere are the spherical harmonic functions (or spherical harmonics for short).…”

Section: Introductionmentioning

confidence: 99%

Spatial-Slepian Transform on the Sphere

Aslam,

Khalid

2020

Preprint

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We present spatial-Slepian transform (SST) for the representation of signals on the sphere to support localized signal analysis. We use well-optimally concentrated Slepian functions, obtained by solving the Slepian spatial-spectral concentration problem of finding bandlimited and spatially optimally concentrated functions on the sphere, to formulate the proposed transform and obtain the joint spatial-Slepian domain representation of the signal. Due to the optimal energy concentration of the Slepian functions in the spatial domain, the proposed spatial-Slepian transform allows us to probe spatially localized content of the signal. Furthermore, we present an inverse transform to recover the signal from the spatial-Slepian coefficients, and show that well-optimally concentrated rotated Slepian functions form a tight frame on the sphere. We develop an algorithm for the fast computation of the spatial-Slepian transform and carry out computational complexity analysis. We present the formulation of SST for zonal Slepian functions, which are spatially optimally concentrated in the polar cap (axisymmetric) region, and provide an illustration using the Earth topography map. To demonstrate the utility of the proposed transform, we carry out localized variation analysis; employing SST for detecting hidden localized variations in the signal.

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Efficient Representation of Head-Related Transfer Functions With Combination of Spherical Harmonics and Spherical Wavelets

Cited by 10 publications

References 33 publications

Interaural Time Difference Prediction Using Anthropometric Interaural Distance

Interaural Time Difference Prediction Using Anthropometric Interaural Distance

Hippocampus shape characterization with 3D Zernike transformation in clinical Alzheimer's disease progression

Spatial-Slepian Transform on the Sphere

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