Perspective Chapter: Modern Acquisition of Personalised Head-Related Transfer Functions – An Overview

Pollack, Katharina; Kreuzer, W.; Majdak, Piotr

doi:10.5772/intechopen.102908

Cited by 12 publications

(15 citation statements)

References 144 publications

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“…Previous works to characterize individual HRTFs have been mainly categorized into two different approaches [13]. The first is the most precise method to obtain individual HRTFs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

et al. 2023

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The generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric information on pinnae. Most of the studies focus their personal auditory optimization analysis on the study of amplitude versus frequency on HRTFs, mainly in the search for significant elevation cues of frequency maps. Therefore, knowing the HRTFs of each individual is of considerable help to improve sound quality. The following work proposes a methodology to model HRTFs according to the individual structure of pinnae using multilayer perceptron and linear regression techniques. It is proposed to generate several models that allow knowing HRTFs amplitude for each frequency based on the personal anthropometric data on pinnae, the azimuth angle, and the elevation of the sound source, thus predicting frequency magnitudes. Experiments show that the prediction of new personal HRTF generates low errors, thus this model can be applied to new heads with different pinnae characteristics with high confidence. Improving the results obtained with the standard KEMAR pinna, usually used in cases where there is a lack of information.

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“…Previous works to characterize individual HRTFs have been mainly categorized into two different approaches [13]. The first is the most precise method to obtain individual HRTFs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

et al. 2023

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“…The last two are surface scanning techniques based on line-of-sight propagation; hence, inherently hindered by the occluded pinna geometry. Nonetheless, previous studies have shown that laser scanning is capable of creating suitable geometries for HRTF computation [16,17]. The most precise scanners typically require specialised equipment and long scanning time, making their use impractical outside of a scientific framework [14].…”

Section: Introductionmentioning

confidence: 99%

“…However, the widespread availability of camera sensors suited for this scanning technique, e.g. smartphone cameras, makes this method one of the most appealing for a large-scale application [14,16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of laser scanning and photogrammetric scanning accuracy on the numerical determination of Head-Related Transfer Functions of a dummy head

Di Giusto,

van Ophem,

Desmet

et al. 2023

Acta Acust.

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Individual Head-Related Transfer Functions (HRTFs) are necessary for the accurate rendering of virtual scenes. However, their acquisition is challenging given the complex pinna shape. Numerical methods can be leveraged to compute HRTFs on meshes originating from precise scans of a subject. Although photogrammetry can be used for the scanning, its inaccuracy might affect the spatial cues of simulated HRTFs. This paper aims to assess the significance of the photogrammetric error affecting a Neumann KU100 dummy head scan. The geometrical differences between the photogrammetric scan and a laser scan are mainly located at the pinna cavities. The computed photogrammetric HRTFs, compared to measured and simulated data using objective and perceptually inspired metrics, show deviation in high frequency spectral features, stemming from the photogrammetric scanning error. This spectral deviation hinders the modelled elevation perception with photogrammetric HRTFs to levels comparable to renderings with nonindividual data. Extracting the photogrammetric geometry at individual ear cavities and merging it to the laser mesh, an assessment of the influence of the inaccuracy at different pinna structures is conducted. Correlation analysis between acoustic and geometrical metrics computed on the results is used to identify the most relevant geometrical metrics in relation to the HRTFs.

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“…In fact, the interaural features are particularly informative about the horizontal dimension [1] though rather ambiguous with respect to the vertical dimension, where evidence from the monaural spectral features is more important [12]. This anisotropic relevance of different features is the reason for why specific auditory features are often studied along a single dimension of the so-called modified interaural-polar coordinate system [20], with the lateral angle along the horizontal left/right dimension and the polar angle along the vertical and front/back dimension. However, this geometric separation is a simplification.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian model for human directional localization of broadband static sound sources

Barumerli

Majdak

Geronazzo

et al. 2022

Preprint

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Human listeners estimate the spatial direction of a sound source from multiple auditory features and prior information on the sound direction. In this work, we describe a model of directional localization of a broadband and stationary sound source presented in an anechoic environment to a static listener. The model is based on Bayesian inference and it infers the sound direction from the spatial percept built by weighting the sensory evidence with prior beliefs. With the imposed restrictions, the sensory evidence is composed by a simplistic extraction mechanisms for two binaural features evaluating interaural disparities in time and intensity with a more detailed extraction mechanisms of monaural spectral-shape features. Our analysis considered two model variants for monaural features: amplitude spectra and their spectral gradient profiles. In addition, the model accounts for two types of uncertainties: the sensory noise in spatial cues and the response noise of pointing towards the direction. The proposed model variants were fitted to individual performance of human listeners localizing noise bursts. We then tested the predictive power of those models on the effects of localizing with other ears and sounds with rippled spectrum. This evaluation, especially with the deviation from a flat source spectrum, showed a clear preference for the model variant with spectral gradient cues, also in comparison to previously proposed localization models. Hence, the proposed model provides a tool for the evaluation of head-related transfer functions and a solid basis for future extensions towards modeling dynamic listening conditions.

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Perspective Chapter: Modern Acquisition of Personalised Head-Related Transfer Functions – An Overview

Cited by 12 publications

References 144 publications

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

Analysis of laser scanning and photogrammetric scanning accuracy on the numerical determination of Head-Related Transfer Functions of a dummy head

A Bayesian model for human directional localization of broadband static sound sources

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