Rapid Auditory System Adaptation Using a Virtual Auditory Environment

Toward orthogonal non-individualised head-related transfer functions for forward and backward directional sound: cluster analysis and an experimental study

BackgroundIn order to present virtual sound sources via headphones spatially, head-related transfer functions (HRTFs) can be applied to audio signals. In this so-called binaural virtual acoustics, the spatial perception may be degraded if the HRTFs deviate from the true HRTFs of the listener.ObjectiveIn this study, participants wearing virtual reality (VR) headsets performed a listening test on the 3D audio perception of virtual audiovisual scenes, thus enabling us to investigate the necessity and influence of the individualization of HRTFs. Two hypotheses were investigated: first, general HRTFs lead to limitations of 3D audio perception in VR and second, the localization model for stationary localization errors is transferable to nonindividualized HRTFs in more complex environments such as VR.MethodsFor the evaluation, 39 subjects rated individualized and nonindividualized HRTFs in an audiovisual virtual scene on the basis of 5 perceptual qualities: localizability, front-back position, externalization, tone color, and realism. The VR listening experiment consisted of 2 tests: in the first test, subjects evaluated their own and the general HRTF from the Massachusetts Institute of Technology Knowles Electronics Manikin for Acoustic Research database and in the second test, their own and 2 other nonindividualized HRTFs from the Acoustics Research Institute HRTF database. For the experiment, 2 subject-specific, nonindividualized HRTFs with a minimal and maximal localization error deviation were selected according to the localization model in sagittal planes.ResultsWith the Wilcoxon signed-rank test for the first test, analysis of variance for the second test, and a sample size of 78, the results were significant in all perceptual qualities, except for the front-back position between own and minimal deviant nonindividualized HRTF (P=.06).ConclusionsBoth hypotheses have been accepted. Sounds filtered by individualized HRTFs are considered easier to localize, easier to externalize, more natural in timbre, and thus more realistic compared to sounds filtered by nonindividualized HRTFs.

Adaptation to altered or non-individual head-related transfer functions (HRTFs) has been widely studied in individuals with normal hearing. Studies have shown that after an adaptation period, individuals can significantly improve their sound localization performance with altered cues. Recently, it has been shown that after training with one set of HRTFs, improvements can also be observed with another set, a phenomenon referred to as generalization. This study provides significantly more evidence of this phenomenon by including a larger number of participant groups and a longer training period compared to prior work. The experiment involved four groups of participants: two control groups (one with individual HRTFs, one with non-individual HRTFs) only performing localization tests and two training groups (one with individual HRTFs, one with non-individual HRTFs) completing both localization tests and training sessions. In all cases, the stimulus consisted of a male speech sentence reproduced binaurally. Both training groups showed significant and comparable improvements in localization performance. Moreover, generalization effects were observed and found to be similar between the two training groups.

In perceptual-based techniques for individualization of head-related transfer functions (HRTFs), subjects tune some parameters for several target directions until they achieve an acceptable spatial accuracy. However, this procedure might be time-consuming depending on the ability of the listener, and the number of parameters and target directions. This makes desirable a way to estimate empirically the localization accuracy before tuning sessions. To tackle this problem, we propose a virtual listener based on Support Vector Regression (SVR) to substitute the human listener in such sessions. We show that, using a small training set obtained by sampling uniformly a subject’s HRTFs across directions, our virtual listener achieves human-level localization accuracy. Moreover, simulations show that the virtual listener performance is in accordance with human perception for sound sources with different frequency content as well as sound sources filtered through non-individualized HRTFs. Finally, our approach based on SVR attains performance similar to computationally intensive methods based on Gaussian Process Regression.

Purpose – The purpose of this paper is to present a system for customized binaural audio delivery based on the extraction of relevant features from a 2-D representation of the listener’s pinna. Design/methodology/approach – The most significant pinna contours are extracted by means of multi-flash imaging, and they provide values for the parameters of a structural head-related transfer function (HRTF) model. The HRTF model spatializes a given sound file according to the listener’s head orientation, tracked by sensor-equipped headphones, with respect to the virtual sound source. Findings – A preliminary localization test shows that the model is able to statically render the elevation of a virtual sound source better than non-individual HRTFs. Research limitations/implications – Results encourage a deeper analysis of the psychoacoustic impact that the individualized HRTF model has on perceived elevation of virtual sound sources. Practical implications – The model has low complexity and is suitable for implementation on mobile devices. The resulting hardware/software package will hopefully allow an easy and low-tech fruition of custom spatial audio to any user. Originality/value – The authors show that custom binaural audio can be successfully deployed without the need of cumbersome subjective measurements.

The head-related transfer function(HRTF) is an essential part of spatial auditory display systems. In recent years, numerous HRTF databases are established to include human subjects' measurements, which enabled data-driven research projects such as HRTF prediction and personalization. However, in most cases, each HRTF database has its own unique measuring standard and source direction grid set. It's difficult to merge and efficiently represent the global HRTF information across multiple HRTF databases. Current presentation methods, such as principle component analysis (PCA) and spherical harmonics transform (SHT), are constrained by the source grid layout and regularization errors, which lays burdens for common feature extraction from different sets of HRTF measurements. In this work, we propose a novel approach for the global compact representation of HRTFs across different measuring standards, using hemispherical harmonics (HSH) and conformal mapping. The method takes into account the spatial domain covered by the typical HRTF measurements and proves to be less contained by the source direction grids. Both numerical and auditory model experiments are performed, to examine the representation error and consistency when including multiple HRTF databases with different measuring standards and grid layouts. In continuing work, we are using this method as a pre-processing approach for HRTF personalization utilizing multiple HRTF databases.

Head-related transfer function (HRTF) is characterized as sound transmission from sound source to listener's eardrum. When a listener hears a sound that is filtered with the HRTFs, the listener can localize a virtual target (sound image) as if the sound had come from the position corresponding to that at which the HRTFs were measured. A moving sound image can be generated to switch HRTFs of successive direction in real-time. While many virtual auditory displays (VADs) based on synthesis of HRTFs have been proposed, most of them can synthesize only a few sound images due to lack of computation power. In this article, the VAD system implemented based on graphics processing unit (GPU) was introduced. In our system, the convolution of HRTFs is parallelized on GPU to realize a high-speed processing. In addition, the multiple HRTFs each of which is corresponding to sound sources at different position are processed in parallel to control multiple sound image simultaneously. In this article, the performance of our system was evaluated not only objectively but also subjectively. The results showed that our current system can present at least 40 sound images simultaneously in real-time.

Applications in the creation of virtual auditory spaces (VAS) and sonification require individualized head related transfer functions (HRTFs) for perceptual fidelity. HRTFs exhibit significant variation from person to person due to differences between their pinnae, and their body sizes. We propose and preliminarily implement a simple HRTF customization based on the use of a published database of HRTFs (Algazi et al., 2001) that also contains geometrical measurements of subject pinnae. We measure some of these features via simple image processing, and select the HRTF that has features most closely corresponding to the individual's features. This selection procedure is implemented along with the virtual auditory system described in (Zotkin et al., 2002), and listener tests conducted comparing the customized HRTF and a fixed HRTF. Despite the simplicity of the method, tests reveal average improvement in localization accuracy of about 25 percent, though performance improvement varies with source location and individuals.

Individualization of head-related transfer functions (HRTFs) can improve the quality of binaural applications with respect to the localization accuracy, coloration, and other aspects. Using anthropometric features (AFs) of the head, neck, and pinna for individualization is a promising approach to avoid elaborate acoustic measurements or numerical simulations. Previous studies on HRTF individualization analyzed the link between AFs and technical HRTF features. However, the perceptual relevance of specific errors might not always be clear. Hence, the effects of AFs on perceived perceptual qualities with respect to the overall difference, coloration, and localization error are directly explored. To this end, a listening test was conducted in which subjects rated differences between their own HRTF and a set of nonindividual HRTFs. Based on these data, a machine learning model was developed to predict the perceived differences using ratios of a subject's individual AFs and those of presented nonindividual AFs. Results show that perceived differences can be predicted well and the HRTFs recommended by the models provide a clear improvement over generic or randomly selected HRTFs. In addition, the most relevant AFs for the prediction of each type of error were determined. The developed models are available under a free cultural license.

Research concerned with spatial hearing and binaural sound localization parameters has developed a new approach in dealing with the matter through involving neural networks and making the network localize the sound source. In this process the head related transfer function data of a certain person is used to train the neural network and test its performance. In this paper the authors present the application of artificial neural networks in order to localize the sound using non-individualized head related transfer function, i.e. a network that would achieve a satisfying result concerning the error percentage.

A virtual auditory space can be presented to a listener based on binaural synthesis using head-related transfer functions (HRTFs) that are obtainable by measurements or numerical simulations. Due to hardware complexity, HRTF measurements are typically made for a fixed source distance though they are used in binaural synthesis for variable source distances. However, it is known that HRTFs depend on source distance especially for proximal sources for distance less than 1 m. So it is possible that binaural synthesis with HRTFs for a fixed source distance may result in degradations for proximal sound image perception. In this paper, experiments were performed to examine how the use of distant-variant or -invariant HRTFs affect the perception of a proximal sound source in a virtual auditory space in which the listener’s motion is compensated by head tracking. HRTFs for source distances up to 1 m, in 5 cm steps, are numerically simulated using the boundary element method. Results show the difference between presented and perceived source distances being significantly smaller when distance-variant HRTFs were used. This indicates that the use of HRTFs corresponding to actual sound source position leads to accurate perception of a proximal source.

Head-related transfer functions of rabbits within the front horizontal plane

It is widely acknowledged that individualized head-related transfer function (HRTF) measurements are needed to adequately capture all of the 3D spatial hearing cues. However, many perceptual studies have shown that localization accuracy in the lateral dimension is only minimally decreased by the use of non-individualized head-related transfer functions. This evidence supports the idea that the individualized components of an HRTF could be isolated from those that are more general in nature. In the present study we decomposed the HRTF at each location into average, lateral and intraconic spectral components, along with an ITD in an effort to isolate the sound localization cues that are responsible for the inter-individual differences in localization performance. HRTFs for a given listener were then reconstructed systematically with components that were both individualized and non-individualized in nature, and the effect of each modification was analyzed via a virtual localization test where brief 250 ms noise bursts were rendered with the modified HRTFs. Results indicate that the cues important for individualization of HRTFs are contained almost exclusively in the intraconic portion of the HRTF spectra and localization is only minimally affected by introducing non-individualized cues into the other HRTF components. These results provide new insights into what specific inter-individual differences in head-related acoustical features are most relevant to sound localization, and provide a framework for how future human-machine interfaces might be more effectively generalized and/or individualized.

It is commonly believed that near-field head-related transfer functions (HRTFs) provide perceptual benefits over far-field HRTFs that enhance the plausibility of binaural rendering of nearby sound sources. However, to the best of our knowledge, no study has systematically investigated whether using near-field HRTFs actually provides a perceptually more plausible virtual acoustic environment. To assess this question, we conducted two experiments in a six-degrees-of-freedom multimodal augmented reality experience where participants had to compare non-individual anechoic binaural renderings based on either synthesized near-field HRTFs or intensity-scaled far-field HRTFs and judge which of the two rendering methods led to a more plausible representation. Participants controlled the virtual sound source position by moving a small handheld loudspeaker along a prescribed trajectory laterally and frontally near the head, which provided visual and proprioceptive cues in addition to the auditory cues. The results of both experiments show no evidence that near-field cues enhance the plausibility of non-individual binaural rendering of nearby anechoic sound sources in a dynamic multimodal virtual acoustic scene as examined in this study. These findings suggest that, at least in terms of plausibility, the additional effort of including near-field cues in binaural rendering may not always be worthwhile for virtual or augmented reality applications.

Head-Related Transfer Function (HRTF) plays a critical role in how the auditory system perceives spatial information. The spectral cues embedded in HRTF are vital for accurately determining the elevation of sound sources. In existing approaches, deep neural networks (DNNs) have been utilized to predict the magnitude spectra of HRTF from images of the pinna, typically employing the HRTF log-magnitude as the output during training. However, HRTF encompasses the acoustic characteristics of both the head and torso, exhibiting direction-dependent patterns that pose challenges in reconstructing its spectral cues. To address this complexity, we propose an innovative method for HRTF individualization. Our model uses Pinna-Related Transfer Function (PRTF) as the output during training, which helps alleviate the impact of sound reflections from the head and torso in the head-related impulse response (HRIR). Our experimental findings, based on an HRTF dataset, illustrate that our proposed model excels in reconstructing the first and second spectral cues. Furthermore, it outperforms previous deep learning models in terms of log spectral distortion (LSD).