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.
The potential of virtual reality (VR) in supporting hearing research and audiological care has long been recognized. While allowing the creation of experimental settings that closely resemble real-life scenarios and potentially leading to more ecologically valid results, VR could also support the current need for automated or remote assessment of auditory processing abilities in clinical settings. Understanding speech in competing noise is the most common complaint of patients with hearing difficulties, and the need to develop tools that can simplify speech-in-noise testing by reducing the time and resources required while improving the ecological validity of current assessment procedures is an area of great research interest. However, the use of VR for speech-in-noise testing has not yet been widely adopted because it is still unclear whether subjects respond to virtual stimuli the same way as they would in real-life settings. Using headphone-based binaural presentation, delivering visuals through head-mounted displays (HMDs), and using unsupervised (self-testing or remote) procedures are some aspects of virtualization that could potentially affect speech-in-noise measures, and the extent of this potential impact remains unclear. Before virtualization can be considered feasible, its effects on behavioral psychoacoustic measures must be understood. Thus, the ability to reproduce results from typical laboratory and clinical settings in VR environments is a major topic of current research. In this study, we sought to answer whether it is possible to reproduce results from a standard speech-in-noise test using state-of-the-art technology and commercially available VR peripherals. To this end, we compared the results of a well-established speech-in-noise test conducted in a conventional loudspeaker-based laboratory setting with those obtained in three different virtual environments. In each environment, we introduced one aspect of virtualization, i.e., virtual audio presentation in the first environment, HMD-based visuals with a visual anchor representing the target speaker in the second, and an alternative feedback- and scoring method allowing unsupervised testing in the last. Our results indicate that the speech-in-noise measures from the loudspeaker-based measurement and those from the virtual scenes were all statistically identical, suggesting that conducting speech-in-noise testing in state-of-the-art VR environments may be feasible even without experimenter supervision.
High-quality rendering of spatial sound fields in real-time is becoming increasingly important with the steadily growing interest in virtual and augmented reality technologies. Typically, a spherical microphone array (SMA) is used to capture a spatial sound field. The captured sound field can be reproduced over headphones in real-time using binaural rendering, virtually placing a single listener in the sound field. Common methods for binaural rendering first spatially encode the sound field by transforming it to the spherical harmonics domain and then decode the sound field binaurally by combining it with head-related transfer functions (HRTFs). However, these rendering methods are computationally demanding, especially for high-order SMAs, and require implementing quite sophisticated real-time signal processing. This paper presents a computationally more efficient method for real-time binaural rendering of SMA signals by linear filtering. The proposed method allows representing any common rendering chain as a set of precomputed finite impulse response filters, which are then applied to the SMA signals in real-time using fast convolution to produce the binaural signals. Results of the technical evaluation show that the presented approach is equivalent to conventional rendering methods while being computationally less demanding and easier to implement using any real-time convolution system. However, the lower computational complexity goes along with lower flexibility. On the one hand, encoding and decoding are no longer decoupled, and on the other hand, sound field transformations in the SH domain can no longer be performed. Consequently, in the proposed method, a filter set must be precomputed and stored for each possible head orientation of the listener, leading to higher memory requirements than the conventional methods. As such, the approach is particularly well suited for efficient real-time binaural rendering of SMA signals in a fixed setup where usually a limited range of head orientations is sufficient, such as live concert streaming or VR teleconferencing.
Sound localization testing is key for comprehensive hearing evaluations, particularly in cases of suspected auditory processing disorders. However, sound localization is not commonly assessed in clinical practice, likely due to the complexity and size of conventional measurement systems, which require semicircular loudspeaker arrays in large and acoustically treated rooms. To address this issue, we investigated the feasibility of testing sound localization in virtual reality (VR). Previous research has shown that virtualization can lead to an increase in localization blur. To measure these effects, we conducted a study with a group of normal-hearing adults, comparing sound localization performance in different augmented reality and VR scenarios. We started with a conventional loudspeaker-based measurement setup and gradually moved to a virtual audiovisual environment, testing sound localization in each scenario using a within-participant design. The loudspeaker-based experiment yielded results comparable to those reported in the literature, and the results of the virtual localization test provided new insights into localization performance in state-of-the-art VR environments. By comparing localization performance between the loudspeaker-based and virtual conditions, we were able to estimate the increase in localization blur induced by virtualization relative to a conventional test setup. Notably, our study provides the first proxy normative cutoff values for sound localization testing in VR. As an outlook, we discuss the potential of a VR-based sound localization test as a suitable, accessible, and portable alternative to conventional setups and how it could serve as a time- and resource-saving prescreening tool to avoid unnecessarily extensive and complex laboratory testing.