Pinna-related transfer functions and lossless wave equation using finite-difference methods: Verification and asymptotic solution

Prepelita, Sebastian; Bolaños, Javier Gómez; Geronazzo, Michele; Mehra, Ravish; Savioja, Lauri

doi:10.1121/1.5131245

Cited by 12 publications

(19 citation statements)

References 239 publications

(531 reference statements)

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“…CNN-Reg and UNet-Reg both offer computationally effective alternatives to simulations with an average tolerance of 1.38dB and 1.52dB SDE in prediction. We do note that simulations themselves are shown to have errors at high frequencies [25], and so some of this error might be due to the noise in ground truth data, rather than model's incapacity in estimation. This also relates to increasing SDE vs. frequency in Figures 2 and 3.…”

Section: Effect Of Voxelizationmentioning

confidence: 91%

On the Predictability of Hrtfs from Ear Shapes Using Deep Networks

Zhou

Jiang

Ithapu

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Head-Related Transfer Function (HRTF) individualization is critical for immersive and realistic spatial audio rendering in augmented/virtual reality. Neither measurements nor simulations using 3D scans of head/ear are scalable for practical applications. More efficient machine learning approaches are being explored recently, to predict HRTFs from ear images or anthropometric features. However, it is not yet clear whether such models can provide an alternative for direct measurements or high-fidelity simulations. Here, we aim to address this question. Using 3D ear shapes as inputs, we explore the bounds of HRTF predictability using deep neural networks. To that end, we propose and evaluate two models, and identify the lowest achievable spectral distance error when predicting the true HRTF magnitude spectra.

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Section: Effect Of Voxelizationmentioning

confidence: 91%

On the Predictability of Hrtfs from Ear Shapes Using Deep Networks

Zhou

Jiang

Ithapu

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Essentially, this implies analyzing the discretization error behavior by comparing the observed (from FDTD simulations) with the expected (i.e., the formal order of the employed scheme) rate at which the discretization error decreases as a function of spatial grid spacing. 5 Note that a first-order convergence rate is expected due to the first-order voxelization error 14 at the boundaries as well as the nearest neighbor error (i.e., the computations are done at voxel centers, and no spatial interpolation is done; see, e.g., Table 1 from Ref. 5).…”

Section: Convergence Rate Estimationmentioning

confidence: 99%

“…It is recalled that the convergence of the voxelized geometry is of first order. 5 As such, the first-order accuracy observed for the interpolated data implies that the voxelization error is dominant.…”

Section: Spatial Interpolationmentioning

confidence: 99%

“…Moreover, studies focused on verification tasks in the context of HRTF predictions are scarce. 5 In Ref. 6, FDTD-simulated HRTFs of a single sphere were only visually compared to analytic solutions, and no convergence rate was estimated nor asymptotic solution predicted.…”

Section: Introductionmentioning

confidence: 99%

“…However, the code verification in Ref. 5 was done in the absence of the voxelization error. As such, the case of the single sphere model is relevant since code verification is possible as there exists an analytic solution for the problem of scattering from such a model.…”

Section: Introductionmentioning

confidence: 99%

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Finite-difference time-domain simulations: Verification on head-related transfer functions of a rigid sphere model

Meyer

Smirnov

Khajeh-Saeed

et al. 2022

JASA Express Letters

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This paper presents a verification procedure for finite-difference time-domain-simulated head-related transfer functions (HRTFs) from a simplified model of a human head, a sphere. The analytic solution required by the code verification is computed with the multipole reexpansion technique and used to estimate convergence rates. A solution verification process follows in which asymptotic predictions are computed. For the HRTFs considered and employed grids, results show that the convergence rates attain the expected first-order accuracy at lower frequencies, after which scattered estimates are observed. Results also reveal that the asymptotic predictions are accurate up to 10 kHz, after which bias is observed.

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Sonic Interactions in Virtual Environments: The Egocentric Audio Perspective of the Digital Twin

Geronazzo

Serafin

2022

Human–Computer Interaction Series

Self Cite

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The relationships between the listener, physical world, and virtual environment (VE) should not only inspire the design of natural multimodal interfaces but should be discovered to make sense of the mediating action of VR technologies. This chapter aims to transform an archipelago of studies related to sonic interactions in virtual environments (SIVE) into a research field equipped with a first theoretical framework with an inclusive vision of the challenges to come: the egocentric perspective of the auditory digital twin. In a VE with immersive audio technologies implemented, the role of VR simulations must be enacted by a participatory exploration of sense-making in a network of human and non-human agents, called actors. The guardian of such locus of agency is the auditory digital twin that fosters intra-actions between humans and technology, dynamically and fluidly redefining all those configurations that are crucial for an immersive and coherent experience. The idea of entanglement theory is here mainly declined in an egocentric spatial perspective related to emerging knowledge of the listener’s perceptual capabilities. This is an actively transformative relation with the digital twin potentials to create movement, transparency, and provocative activities in VEs. The chapter contains an original theoretical perspective complemented by several bibliographical references and links to the other book chapters that have contributed significantly to the proposal presented here.

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Pinna-related transfer functions and lossless wave equation using finite-difference methods: Verification and asymptotic solution

Cited by 12 publications

References 239 publications

On the Predictability of Hrtfs from Ear Shapes Using Deep Networks

On the Predictability of Hrtfs from Ear Shapes Using Deep Networks

Finite-difference time-domain simulations: Verification on head-related transfer functions of a rigid sphere model

Sonic Interactions in Virtual Environments: The Egocentric Audio Perspective of the Digital Twin

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