Perceptually Motivated Analysis of Numerically Simulated Head-Related Transfer Functions Generated By Various 3D Surface Scanning Systems

Dinakaran, Manoj; Brinkmann, Fabian; Harder, Stine; Pelzer, Robert; Grosche, Peter; Paulsen, Rasmus Reinhold; Weinzierl, Stefan

doi:10.1109/icassp.2018.8461789

Cited by 13 publications

(19 citation statements)

References 8 publications

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“…is important to note that this ear-alignment process is invertible, which means that going from h LnR to h LnR a and back can be performed without any loss of information. Figure 2 presents an example of the SH spectrum of a KEMAR HRTF [26,52], for the basic and ear-aligned HRTF representations. The SH spectrum, which is the energy of the SH coefficients at every order n, is computed as: and normalized by the maximum value for each frequency.…”

Section: Hrtf Ear-alignmentmentioning

confidence: 99%

Binaural Reproduction Based on Bilateral Ambisonics

Ben-Hur¹,

Alon²,

Berebi³

et al. 2022

Advances in Fundamental and Applied Research on Spatial Audio

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Binaural reproduction of high-quality spatial sound has gained considerable interest with the recent technology developments in virtual and augmented reality. The reproduction of binaural signals in the Spherical-Harmonics (SH) domain using Ambisonics is now a well-established methodology, with flexible binaural processing realized using SH representations of the sound-field and the Head-Related Transfer Function (HRTF). However, in most practical cases, the binaural reproduction is order-limited, which introduces truncation errors that have a detrimental effect on the perception of the reproduced signals, mainly due to the truncation of the HRTF. Recently, it has been shown that manipulating the HRTF phase component, by ear-alignment, significantly reduces its effective SH order while preserving its phase information, which may be beneficial for alleviating the above detrimental effect. Incorporating the ear-aligned HRTF into the binaural reproduction process has been suggested by using Bilateral Ambisonics, which is an Ambisonics representation of the sound-field formulated at the two ears. While this method imposes challenges on acquiring the sound-field, and specifically, on applying head-rotations, it leads to a significant reduction in errors caused by the limited-order reproduction, which yields a substantial improvement in the perceived binaural reproduction quality even with first order SH.

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Section: Hrtf Ear-alignmentmentioning

confidence: 99%

Binaural Reproduction Based on Bilateral Ambisonics

Ben-Hur¹,

Alon²,

Berebi³

et al. 2022

Advances in Fundamental and Applied Research on Spatial Audio

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

“…Dinakaran et al [288] employed the Burton-Miller BEM formulation to compute the HRTFs of the FABIAN dummy head mesh obtained with six scanning methods. Using one method as the reference, [288] reports smoothed HRTF magnitude errors, broadband ILD errors, and broadband ITD errors. Moreover, [288] also used Baumgartner's model [280] to quantify the differences in obtained HRTFs: such results did not correlate well with the HRTF magnitude errors smoothed with auditory filters.…”

mentioning

confidence: 99%

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

Prepelita

Bolaños

Geronazzo

et al. 2019

The Journal of the Acoustical Society of America

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The public defense on 29th May 2020 at 16:00 (4 p.m.) will be organized via remote technology. Link: https://aalto.zoom.us/j/66896959534 Zoom Quick Guide: https://www.aalto.fi/en/services/zoomquick-guide A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Science, at a public examination held online via video conferencing on 29 May 2017 at 16 (EEST).

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“…We use three different 3D tensor sizes: 16 3 , 32 3 and 64 3 , which correspond to 5.6mm, 2.8mm and 1.4mm per voxel respectively. [20] showed that scan precision of approx. 4mm is sufficient to maintain overall spectral shape of simulated HRTF (although 16 3 setting is just above this threshold, we use it to investigate models' representative capacity).…”

Section: Dataset and Data Representationmentioning

confidence: 99%

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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Perceptually Motivated Analysis of Numerically Simulated Head-Related Transfer Functions Generated By Various 3D Surface Scanning Systems

Cited by 13 publications

References 8 publications

Binaural Reproduction Based on Bilateral Ambisonics

Binaural Reproduction Based on Bilateral Ambisonics

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

On the Predictability of Hrtfs from Ear Shapes Using Deep Networks

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