Head-related transfer functions (HRTFs) are central to convincing binaural rendering in virtual and augmented reality applications. While individual HRTFs offer the highest perceptual fidelity, the practical difficulty of personal HRTF acquisition drives widespread use of dummy head (mannequin) measurements as a non-individualized substitute. Despite their ubiquity, systematic perceptual benchmarking of dummy head HRTFs against human HRTFs remains limited, particularly with respect to whether consistent trends emerge across listeners irrespective of individual HRTF preference. This study extends prior work on subjective HRTF evaluation methodologies and perceptual ranking by applying an established trajectory-based quality assessment paradigm to a mixed set of dummy head and human HRTFs. Participants were presented with predefined auditory trajectories rendered via binaural synthesis and asked to rate the perceptual quality of each rendering with respect to adherence to the prescribed trajectory. HRTFs were presented in randomised order across two sets of eight, with repeated items serving as an inter-set normalisation anchors. The HRTF pool encompassed human measurements alongside a range of dummy head types: simplified head-only geometries, head-and-torso simulators (HATS), and models incorporating absorptive materials (hair, clothing analogues). The primary research question is whether, despite well-documented listener-dependent variability in HRTF suitability, population-level trends differentiate dummy head HRTFs from human ones, and further, whether acoustic complexity of the mannequin (torso, absorptive surfaces) correlates with perceptual performance. Results are discussed in terms of implications for HRTF database design and substitute HRTF selection strategies for immersive audio applications.
In head-related transfer functions (HRTFs), spectral cues due to the individual pinna geometry are known to contribute to elevation perception and externalization. The pinna component of an HRTF is referred to as a pinna-related transfer function (PRTF). Some headphone concepts aim to excite individual PRTF cues by placing the headphone transducer away from the traditional position on the interaural axis, e.g. tilted in front of the pinna. However, it is not clear to which extent the individual PRTF is preserved when the pinna is placed inside a headphone cup enclosed by a baffle and a cushion. In this study, multiple prototype setups successively approximating a headphone cup and allowing for variable transducer positions are analyzed using a set of silicone pinna replicas. PRTF perturbations are analyzed in near field measurements and the impact of headphone cup acoustics is discussed. Based on the observation that the perturbations are systematic, an equalization scheme restoring the free field PRTF based on the median of measurements with several pinnae is proposed.
Virtual, augmented, and mixed reality experiences are becoming more commonplace as consumer-grade devices proliferate. Head-Related Transfer Functions (HRTFs) are used to create realistic spatial audio in virtual and augmented environments. Mathematically, HRTFs represent solutions to acoustic boundary-value scattering problems governed by the Helmholtz equation. Neural operators are neural networks designed to learn the solutions of partial differential equations (PDEs). The present work proposes an operator-learning framework based on the Deep Operator Network (DeepONet) for individualized HRTF prediction. By implementing a non-uniform sampling strategy for 3-D head meshes and data compression along the frequency axis, the framework achieves high-fidelity predictions while reducing data dimensionality. Our method shows low log-spectral distortion, generalizes to unseen spatial grids, and infers an entire head’s HRTF field in ~0.3 seconds. Objective evaluations demonstrate the framework's effectiveness in personalization and spatial interpolation. Furthermore, robust performance on unseen subjects and coordinates highlights the model's generalization capability, offering a computationally efficient alternative for HRTFs personalization.
Accurate loudness estimation is essential for audio production, quality control, and loudness compliance, but no established recommendation exists for binaural playback over headphones. This paper investigates the influence of binauralizers and HRTF processing on objective loudness estimation for binauralized Ambisonics content. Two experiments were conducted using 163 Ambisonics clips binauralized with two open-source renderers and three HRTF sets under three HRTF preprocessing conditions. Objective loudness metrics were compared against ground truth loudness data derived from 7.1+4 loudspeaker feeds according to ITU-R BS.1770. Results reveal small to moderate differences in Integrated Loudness and larger differences in the True Peak values between the evaluated binauralizers, and that diffuse-field equalization can effectively eliminate loudness and True Peak differences across binauralizers and across sets of HRTFs. The findings can help to better predict and ensure loudness compliance in binauralized audio consumption in XR and gaming, especially when importing 3rd-party HRTFs is supported.
Head-Related Transfer Functions (HRTFs) are commonly measured at the blocked ear canal entrance, assuming that the ear canal transfer function is direction-independent. While this assumption holds well at low and mid frequencies, its validity at high frequencies has been questioned. A recent pilot study on a single pair of 3D-printed ear replicas found evidence of directional effects above 9 kHz, but was limited in scope. This study extends that work using 3D-printed ear replicas of ten subjects from the IHA database, mounted on a dummy head. Ear canal transfer functions were measured across a full spherical grid of 1944 incidence angles. Results reveal significant directional variability above 6–7 kHz, with standard deviations of 6 –8 dB at resonant frequencies. High measurement repeatability confirms these are genuine directional effects rather than measurement artifacts. The directional behavior is consistently observed across all subjects and appears linked to the second and higher ear canal resonances. These findings suggest that the current state of the art blocked-canal HRTF measurements may omit spatially relevant spectral information above 7 kHz.
Head-related transfer functions (HRTFs) are crucial for plausible binaural audio playback for virtual, augmented, and mixed-reality applications. In such applications, humans showed higher sound-localisation accuracy, higher perceived externalisation, and experience less colouration when using their individual HRTFs compared to non-individual HRTFs. Because high-quality individual HRTFs require cumbersome measurements in specialised facilities, applications often use non-indivdual or dummy-head HRTFs as a practical alternative. Humans are able to adapt to non-individual HRTFs, which leads to a localisation performance comparable to that achieved with individual HRTFs. Therefore, adaptation to non-individual HRTFs could be a practical alternative whenever individual HRTFs are unavailable; However, this would only be possible if the same non-individual standard HRTF was used across different applications. To find out if this is the case, we conducted a survey on HRTF usage among 76 professionals working in the field of spatial audio. The findings suggest that there is currently no de facto standard HRTF. Surprisingly, only half of those with access to individual HRTFs are actually using them, and most would be willing to switch to a default HRTF set if one was established.
Head-related transfer functions (HRTFs) underpin spatial hearing in virtual and augmented reality systems. Whilst individual HRTFs capture listener-specific morphology, their practical limitations have led to widespread use of generic HRTFs and growing interest in synthetic approaches. Yet their relative perceptual impact remains rarely compared within a single study. In this study, twenty listeners completed two virtual reality sound localisation experiments with complementary subsets of interleaved HRTF conditions enabling within-subject comParison of five conditions: individually measured, KEMAR, randomly selected non-individual measured, high-resolution scan-based synthetic and photogrammetry-based synthetic HRTFs. Test–retest stability of the individually measured baseline across sessions supported pooling across experiments and attributing differences to perceptual rather than session effects. Across HRTF conditions, lateral localisation metrics were largely insensitive to HRTF type, whereas polar-domain metrics and confusion rates showed strong HRTF dependence. Random HRTFs outperformed KEMAR on several polar metrics. High-resolution synthetic HRTFs matched individual measured performance, whilst photogrammetry-based synthetic HRTFs, alongside KEMAR, showed the greatest degradation. These findings clarify practical choices for non-individual baselines and highlight the importance of mesh resolution when using numerical synthesis for elevation-dependent localisation tasks.
