Measurement of Head-Related Transfer Functions: A Review

Li, Song; Peissig, Jürgen

doi:10.3390/app10145014

Cited by 53 publications

(47 citation statements)

References 117 publications

(208 reference statements)

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“…For convenience, let us define E hav to be the errors obtained with bias defined in (2). Although choosing up to D principal components is possible, this often leads to overfitting, and the general practice is to choose Q < D such that around 90% of the variance is captured [17,19,27].…”

Section: Spatial Principal Component Analysis (Spca) Factorizesmentioning

confidence: 99%

“…Accurate modeling of the head-related transfer functions (HRTFs) is critical for applications requiring spatial audio. Because of morphological differences, every person has a unique HRTF, and capturing this personalized HRTF is important for accurate spatialization [1,2]. Previous works show that using a generic HRTF for spatialization leads to significant perceptual errors such as in-head localization, front-back confusion, and lowered ability to discriminate source elevations [3].…”

Section: Introductionmentioning

confidence: 99%

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Personalized HRTF Modeling Using DNN-Augmented BEM

Zhang

Wang

James

2021

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

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Accurate modeling of personalized head-related transfer functions (HRTFs) is difficult but critical for applications requiring spatial audio. However, this remains challenging as experimental measurements require specialized equipment, numerical simulations require accurate head geometries and robust solvers, and data-driven methods are hungry for data. In this paper, we propose a new deep learning method that combines measurements and numerical simulations to take the best of three worlds. By learning the residual difference and establishing a high quality spatial basis, our method achieves consistently 2 dB to 2.5 dB lower spectral distortion (SD) compared to the state-of-the-art methods.

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Section: Spatial Principal Component Analysis (Spca) Factorizesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalized HRTF Modeling Using DNN-Augmented BEM

Zhang

Wang

James

2021

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

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show abstract

“…To date, however, anechoic measurement is still a common and the most accurate approach, in particular for individual HRTFs of human subjects. Clean, high-quality HRTFs are traditionally measured in an anechoic chamber and require listener-specific recordings at thousands of spatial positions [7,8]. While these requirements are crucial to enable traditional binaural modeling, data captures are expensive and require highly specialized equipment and capture stages.…”

Section: Introductionmentioning

confidence: 99%

Implicit HRTF Modeling Using Temporal Convolutional Networks

Gebru

Marković

Richard

et al. 2021

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

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Estimation of accurate head-related transfer functions (HRTFs) is crucial to achieve realistic binaural acoustic experiences. HRTFs depend on source/listener locations and are therefore expensive and cumbersome to measure; traditional approaches require listenerdependent measurements of HRTFs at thousands of distinct spatial directions in an anechoic chamber. In this work, we present a datadriven approach to learn HRTFs implicitly with a neural network that achieves state of the art results compared to traditional approaches but relies on a much simpler data capture that can be performed in arbitrary, non-anechoic rooms. Despite that simpler and less acoustically ideal data capture, our deep learning based approach learns HRTF of high quality. We show in a perceptual study that the produced binaural audio is ranked on par with traditional DSP approaches by humans and illustrate that interaural time differences (ITDs), interaural level differences (ILDs) and spectral clues are accurately estimated.

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“…The availability of relatively low-cost hardware technologies for immersive visualization has shown the need for higher fidelity HRTF-based spatial sound simulation. Although recent advances in fast HRTF acquisition make it possible to capture HRTFs via acoustic measurement in minutes [2], [3], individual HRTFs are still hard to obtain for the general public. As a consequence, most applications have been relying on non-individual, or generic, HRTFs.…”

Section: Introductionmentioning

confidence: 99%

“…For more details on the pinna casting procedure, please consult[46] 2. https://sagamuseum.is/sagadesign/index.html…”

mentioning

confidence: 99%

Estimation of Spectral Notches From Pinna Meshes: Insights From a Simple Computational Model

Spagnol

Miccini

Onofrei

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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While previous research on spatial sound perception investigated the physical mechanisms producing the most relevant elevation cues, how spectral notches are generated and related to the individual morphology of the human pinna is still a topic of debate. Correctly modeling these important elevation cues, and in particular the lowest frequency notches, is an essential step for individualizing Head-Related Transfer Functions (HRTFs).In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. We apply such a model to a highly controlled HRTF dataset built with the specific purpose of understanding the contribution of the pinna to the HRTF. Results show that the computational model is able to approximate the lowest frequency notch with improved accuracy with respect to other state-of-the-art methods. By contrast, the model fails to predict higher-order pinna notches correctly. The proposed approximation supplements understanding of the morphology involved in generating spectral notches in experimental HRTFs.

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Measurement of Head-Related Transfer Functions: A Review

Cited by 53 publications

References 117 publications

Personalized HRTF Modeling Using DNN-Augmented BEM

Personalized HRTF Modeling Using DNN-Augmented BEM

Implicit HRTF Modeling Using Temporal Convolutional Networks

Estimation of Spectral Notches From Pinna Meshes: Insights From a Simple Computational Model

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