Simplifying head-related transfer function measurements: A system for use in regular rooms based on free head movements

Bau, David; Lübeck, Tim; Arend, Johannes M.; Dziwis, Damian; Pörschmann, Christoph

doi:10.1109/i3da48870.2021.9610890

Cited by 3 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We investigated the interpolation error of HRTF data sampled on six irregular sampling grids as shown in Figure 1. The grids are representative example results of our FBM system (Bau et al, 2021), where subjects followed different strategies to obtain spherical sampling. The first two grids Irregular 1 and Irregular 2, each with 40 sampling points, can be considered as very sparse and represent a minimal configuration.…”

Section: Interpolation Analysismentioning

confidence: 99%

“…Reijniers et al (2020) presented a system that uses an optical tracking system for the head movements and short ESS for measurements at discrete sampling points. Similarly, our recently proposed system (Bau et al, 2021) is based on ESS measurements but uses a commercially available virtual reality system for tracking. With the latter two systems, measurements can be performed even in reverberant conditions, meaning that an anechoic chamber is not mandatory.…”

Section: Introductionmentioning

confidence: 99%

“…This study investigates SH interpolation of selected irregular sampling grids obtained from HRTF measurements with our FBM system (Bau et al, 2021) to find the optimal SH order for interpolation. The sparse sets are spatially interpolated to a dense reference grid using state-of-the-art pre-and postprocessing methods in combination with SH interpolation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of the Optimal Spherical Harmonics Order for the Interpolation of Head-Related Transfer Functions Sampled on Sparse Irregular Grids

Bau

Arend²,

Pörschmann³

2022

Front. Signal Process.

View full text Add to dashboard Cite

Conventional individual head-related transfer function (HRTF) measurements are demanding in terms of measurement time and equipment. For more flexibility, free body movement (FBM) measurement systems provide an easy-to-use way to measure full-spherical HRTF datasets with less effort. However, having no fixed measurement installation implies that the HRTFs are not sampled on a predefined regular grid but rely on the individual movements of the subject. Furthermore, depending on the measurement effort, a rather small number of measurements can be expected, ranging, for example, from 50 to 150 sampling points. Spherical harmonics (SH) interpolation has been extensively studied recently as one method to obtain full-spherical datasets from such sparse measurements, but previous studies primarily focused on regular full-spherical sampling grids. For irregular grids, it remains unclear up to which spatial order meaningful SH coefficients can be calculated and how the resulting interpolation error compares to regular grids. This study investigates SH interpolation of selected irregular grids obtained from HRTF measurements with an FBM system. Intending to derive general constraints for SH interpolation of irregular grids, the study analyzes how the variation of the SH order affects the interpolation results. Moreover, the study demonstrates the importance of Tikhonov regularization for SH interpolation, which is popular for solving ill-posed numerical problems associated with such irregular grids. As a key result, the study shows that the optimal SH order that minimizes the interpolation error depends mainly on the grid and the regularization strength but is almost independent of the selected HRTF set. Based on these results, the study proposes to determine the optimal SH order by minimizing the interpolation error of a reference HRTF set sampled on the sparse and irregular FBM grid. Finally, the study verifies the proposed method for estimating the optimal SH order by comparing interpolation results of irregular and equivalent regular grids, showing that the differences are small when the SH interpolation is optimally parameterized.

show abstract

Section: Interpolation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the Optimal Spherical Harmonics Order for the Interpolation of Head-Related Transfer Functions Sampled on Sparse Irregular Grids

Bau

Arend²,

Pörschmann³

2022

Front. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Conventional measurement systems for acquiring individual HRTFs could thus use fewer loudspeakers and avoid rotation or rotate faster. In addition, simplified measurement systems that require significantly less equipment were recently introduced as an alternative to the complex conventional systems [12]- [14]. For such systems, advanced interpolation methods are essential for upsampling the sparse and sometimes irregularly sampled HRTF sets to high-quality dense sets [14]- [16].…”

mentioning

confidence: 99%

Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions

Arend,

Pörschmann,

Weinzierl

et al. 2023

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

View full text Add to dashboard Cite

Head-related transfer functions (HRTFs) are essential for virtual acoustic realities because they contain all cues for localizing sound sources in three-dimensional space. Acoustic measurements are one way to obtain high-quality HRTFs. To reduce measurement time, cost, and complexity of measurement systems, a promising approach is to capture only a few HRTFs on a sparse sampling grid and then upsample them to a dense HRTF set by interpolation. However, HRTF interpolation is challenging because small changes in source position can result in significant changes in the HRTF phase and magnitude response. Previous studies have greatly improved the interpolation by time-aligning the HRTFs in pre-processing, but magnitude interpolation errors remain a problem, especially in contralateral regions. Building on time-aligned interpolation, we propose a post-interpolation magnitude correction derived from a frequency-smoothed HRTF representation. Our technical evaluation based on 96 individual simulated HRTF sets shows that the magnitude correction reduces subject-averaged magnitude errors in the higher frequency range by up to 1.5 dB when averaged over all directions and by up to 4 dB in the contralateral region. As a result, interaural level differences in the upsampled HRTFs are also improved. The accompanying perceptual evaluation shows that the magnitude correction significantly reduces perceived coloration and results in a more stable and accurate perceived source position. Additional technical evaluations show that the proposed method outperforms current machine learning based algorithms, can be used with measured HRTFs, and is superior to using a generic dummy head HRTF set even when only six source positions are used for upsampling.

show abstract

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

Pollack¹,

Kreuzer²,

Majdak³

2022

Advances in Fundamental and Applied Research on Spatial Audio

View full text Add to dashboard Cite

Head-related transfer functions (HRTFs) describe the spatial filtering of acoustic signals by a listener’s anatomy. With the increase of computational power, HRTFs are nowadays more and more used for the spatialised headphone playback of 3D sounds, thus enabling personalised binaural audio playback. HRTFs are traditionally measured acoustically and various measurement systems have been set up worldwide. Despite the trend to develop more user-friendly systems and as an alternative to the most expensive and rather elaborate measurements, HRTFs can also be numerically calculated, provided an accurate representation of the 3D geometry of head and ears exists. While under optimal conditions, it is possible to generate said 3D geometries even from 2D photos of a listener, the geometry acquisition is still a subject of research. In this chapter, we review the requirements and state-of-the-art methods for obtaining personalised HRTFs, focusing on the recent advances in numerical HRTF calculation.

show abstract

Simplifying head-related transfer function measurements: A system for use in regular rooms based on free head movements

Cited by 3 publications

References 10 publications

Estimation of the Optimal Spherical Harmonics Order for the Interpolation of Head-Related Transfer Functions Sampled on Sparse Irregular Grids

Estimation of the Optimal Spherical Harmonics Order for the Interpolation of Head-Related Transfer Functions Sampled on Sparse Irregular Grids

Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions

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

Contact Info

Product

Resources

About