Virtual audio system customization using visual matching of ear parameters

The use of audio systems that employ binaural synthesis with head tracking has become increasingly popular, particularly in virtual reality gaming systems. The binaural synthesis process uses the Head-Related Transfer Functions (HRTF) as an input required to assign the directions of arrival to sounds coming from virtual sound sources in the created virtual environments. Generic HRTFs are often used for this purpose to accommodate all potential listeners. The hypothesis of the research is that the use of individual HRTF in binaural synthesis instead of generic HRTF leads to improved accuracy and quality of virtual sound source localization, thus enhancing the user experience. A novel methodology is proposed that involves the use of dynamic virtual sound sources. In the experiments, the test participants were asked to determine the direction of a dynamic virtual sound source in both the horizontal and vertical planes using both generic and individual HRTFs. The gathered data are statistically analyzed, and the accuracy of localization is assessed with respect to the type of HRTF used. The individual HRTFs of the test participants are measured using a novel and efficient method that is accessible to a broad range of users.

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

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

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.

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.

Efficient prediction of individual head-related transfer functions based on 3D meshes

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).

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

High performance audio and video codecs and ever increasing bandwidths of data communications networks have enabled multi-platform delivery of high definition media content via transmission channels such as terrestrial broadcast, broadband IP networks and mobile telecommunications systems. Media content consumption becomes ubiquitous and more personalized than ever before and audio is often rendered through headphones. Attentions should be paid to sound quality, and therefore high definition virtual auditory space (VAS) is sought after as an ideal solution. Stereophonic and surround sound are still commonplace while spatial audio is in its infancy to meet the demand of 2.5/3 D high definition video. Binaural headphone rendering offers ultimate spatial audio and source localization; however, individual head-related transfer functions (HRTFs), head movement tracking and suitably synthesized signals are needed to achieve its full potential. Most audio recordings to date are intended and hence best for loudspeaker rendering. Good cross-feed filters can be an interim solution to improve headphone user experience. This paper proposes a universal cross-feed filter. HRTFs at specific source locations were first considered to derive the cross-feed filters. User preferred HRTF parameters were also identified with a variety of representative soundtracks. The transfer functions were measured and simplified to derive user preferred universal cross-feed filters for all. Validation tests confirmed improved listening experience.

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.

Head-related transfer functions (HRTFs) reflect the interaction between human anatomical structures and sound waves, and depend on individuals due to differences in anatomical structures among individuals. This work investigates inter-individual differences of spectral cues encoded in median-plane individual HRTFs using principle component analysis (PCA). First, magnitudes of individual HRTFs from the CIPIC database are decomposed and reduced dimensions by PCA. Then, individual expansion weights of PCA are extracted. Finally, the inter-individual differences in expansion weights are analyzed using standard deviation. Results show that the inter-individual spectral difference in HRTFs decreases with increasing expansion degree of PCA, and the spatial distribution of large inter-individual difference is always at low elevations. When the expansion degree is 1 and 2, the mean of individual expansion weights mainly reflects the inter-elevation difference. The same method is also applied to a combined HRTF database including 295 subjects.

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.

The benefit of using individual head-related transfer functions (HRTFs) in binaural audio is well documented with regards to improving localization precision. However, with the increased use of binaural audio in more complex scene renderings, cognitive studies, and virtual and augmented reality simulations, the perceptual impact of HRTF selection may go beyond simple localization. In this study, the authors develop a list of attributes which qualify the perceived differences between HRTFs, providing a qualitative understanding of the perceptual variance of non-individual binaural renderings. The list of attributes was designed using a Consensus Vocabulary Protocol elicitation method. Participants followed an Individual Vocabulary Protocol elicitation procedure, describing the perceived differences between binaural stimuli based on binauralized extracts of multichannel productions. This was followed by an automated lexical reduction and a series of consensus group meetings during which participants agreed on a list of relevant attributes. Finally, the proposed list of attributes was then evaluated through a listening test, leading to eight valid perceptual attributes for describing the perceptual dimensions affected by HRTF set variations.

Incorporating individualized head-related transfer functions (HRTFs) into a high fidelity sound engine can further improve the perceived quality and realism of binaurally-rendered spatial audio. Traditional methods to measure individual HRTFs tend to be cumbersome, expensive and require physical access to the subject. To address these issues, we develop a convolutional neural network model that, given a single photo of an ear, predicts pinna landmarks that can be used to extract anthropometric features commonly used for HRTF personalization, and match to a database of subjects whose HRTFs and pictures are available. We propose and evaluate a system utilizing this model to generate an individualized HRTF using a minimal set of easily obtainable measurements: single photographs of both ears, as well as head and ear scale for matching interaural time difference (ITD). To extend the reach of our database we employ ideas from Kendall shape theory to match ears non-dimensionally, match all ears to right ears, and make corresponding changes to the database HRIRs. We also apply HAT models to the HRIRs to provide better matching.

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.