Individual Head-related Transfer Functions Research Articles

Estimation of the category of notch frequency bins of the individual head-related transfer functions using the anthropometry of the listener’s pinnae

An authentic approach to present three-dimensional acoustical sensation to everyone is to provide head-related transfer functions (HRTFs) that are exactly adapted to each listener. This approach is called the individualization of HRTFs. In order to generate individualized HRTFs, the present study proposes a modeling method of HRTFs that extracts the frequency bins in which spectral notches (N1 and N2) are included. The frequency bins for N1 and N2 were classified into two categories based on the just noticeable difference in notch frequency with regard to the vertical angle perception of a sound image. Then, discriminant analyses were carried out as objective variables of the category of each of the N1 and N2 frequency bins and as explanatory variables of ten anthropometric parameters of pinnae. The results show that the categories of N1 and N2 frequency bins for the front direction can be estimated with accuracies of 83.1% and 77.8%, respectively. For the rear direction, the accuracies of the category estimation of N1 and N2 were 74.3% and 77.1%, respectively.

Fundamental investigation on acquisition of individual Head-Related Transfer Functions based on fabrication technology

Fundamental investigation on acquisition of individual Head-Related Transfer Functions based on fabrication technology

Head-related transfer function recommendation based on perceptual similarities and anthropometric features.

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.

Estimation of the category of notch frequency bins of the individual head-related transfer functions using the anthropometry of the listener’s pinnae

An authentic approach to present three-dimensional acoustical sensation to everyone is to provide head-related transfer functions (HRTFs) that are exactly adapted to each listener. This approach is called the individualization of HRTFs. In order to generate individualized HRTFs, the present study proposes a modeling method of HRTFs that extracts the frequency bins in which spectral notches (N1 and N2) are included. The notch frequency bins for N1 and N2 were classified into two categories based on the just noticeable difference in notch frequency with regard to the vertical angle perception of a sound image. Then, discriminant analyses were carried out as objective variables of the category of each of the N1 and N2 frequency bins and as explanatory variables of ten anthropometric parameters of pinnae. The results show that the categories of N1 and N2 frequency bins can be estimated with accuracies of 78.9% and 77.8%, respectively.

Usability of Individualized Head-Related Transfer Functions in Virtual Reality: Empirical Study With Perceptual Attributes in Sagittal Plane Sound Localization.

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.

Measurement of Head-Related Transfer Functions: A Review

A head-related transfer function (HRTF) describes an acoustic transfer function between a point sound source in the free-field and a defined position in the listener’s ear canal, and plays an essential role in creating immersive virtual acoustic environments (VAEs) reproduced over headphones or loudspeakers. HRTFs are highly individual, and depend on directions and distances (near-field HRTFs). However, the measurement of high-density HRTF datasets is usually time-consuming, especially for human subjects. Over the years, various novel measurement setups and methods have been proposed for the fast acquisition of individual HRTFs while maintaining high measurement accuracy. This review paper provides an overview of various HRTF measurement systems and some insights into trends in individual HRTF measurements.

Modeling of Individual HRTFs Based on Spatial Principal Component Analysis

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.

Efficient Representation and Sparse Sampling of Head-Related Transfer Functions Using Phase-Correction Based on Ear Alignment

With the proliferation of high quality virtual reality systems, the demand for high fidelity spatial audio reproduction has grown. This requires individual head-related transfer functions (HRTFs) with high spatial resolution. Acquiring such HRTFs is not always possible, which motivates the need for sparsely sampled HRTFs. Additionally, real-time applications require compact representation of HRTFs. Recently, spherical-harmonics (SH) has been suggested for efficient interpolation and representation of HRTFs. However, representation of sparse HRTFs with a limited SH order may introduce spatial aliasing and truncation errors, which have a detrimental effect on the reproduced spatial audio. This is because the HRTF is inherently of a high spatial order. One approach to overcome this limitation is to pre-process the HRTF, with the aim of reducing its effective SH order. A recent study showed that order-reduction can be achieved by time-alignment of HRTFs, through numerical estimation of the time delays of the HRTFs. In this paper, a new method for pre-processing HRTFs in order to reduce their effective order is presented. The method uses phase-correction based on ear alignment, by exploiting the dual-centering nature of HRTF measurements. In contrast to time-alignment, the phase-correction is performed parametrically, making it more robust to measurement noise. The SH order reduction and ensuing interpolation errors due to sparse sampling were analyzed for these two methods. Results indicate significant reduction in the effective SH order, where only 100 measurements and order 6 are required to achieve a normalized mean square error below $-$ 10 dB compared to a fully-sampled, high-order HRTF.

Generation of the amplitude spectra of the individual head-related transfer functions in the upper median plane based on the anthropometry of the listener’s pinnae

In order to address individual differences in head-related transfer functions (HRTFs), individualization of HRTFs has been investigated. The amplitude spectra of individual HRTFs for seven directions in the upper median plane for 27 subjects (54 ears) were estimated from twelve pinna anthropometry and partial regression coefficients, which were obtained by multiple regression analyses. The estimated HRTFs had similar prominent notches and peaks to those for the measured early HRTFs. The spectral distortion and the correlation coefficient between the amplitude spectra of the estimated HRTFs and those of the measured early HRTFs ranged from 3.2 to 4.2 dB and 0.83 to 0.95, respectively. The inter-subject spectral difference was 7.4 dB2. Then, multiple regression analyses were carried out again using 24 subjects (48 ears), who were chosen randomly from the full 27 subjects. The amplitude spectra of three naive subjects (six ears), who were not involved in the multiple regression analysis among 27 subjects, were generated using the pinna anthropometry of the subjects and the partial regression coefficients obtained from 24 subjects. The generated HRTFs had similar prominent notches and peaks to those of the measured early HRTFs, although some of the notches were shallow compared with the measured notches. The spectral distortion and the correlation coefficient ranged from 4.6 to 6.2 dB and from 0.72 to 0.91, respectively. The inter-subject spectral difference was 14.0 dB2. The obtained correlation coefficients suggest the potential for generating an outline of the amplitude spectrum of the HRTFs for an unknown listener from the pinnae anthropometry, although there is a certain absolute difference that needs to be reduced between the amplitude spectrum of the generated HRTFs and the measured early HRTFs.

Advancement of Individualized Head-Related Transfer Functions (HRTFs) in Perceiving the Spatialization Cues: Case Study for an Integrated HRTF Individualization Method

Head-related transfer function (HRTF), which varies across individuals at the same direction, has grabbed widespread attention in the field of acoustics and been used in many scenarios. In order to in-depth investigate the performance of individualized HRTFs on perceiving the spatialization cues, this study presents an integrated algorithm to obtain individualized HRTFs, and explores the advancement of such individualized HRTFs in perceiving the spatialization cues through two different binaural experiments. An integrated method for HRTF individualization on the use of Principle Component Analysis (PCA), Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) was presented first. The objective evaluation was then made to verify the algorithmic effectiveness of that method. Next, two subjective experiments were conducted to explore the advancement of individualized HRTFs in perceiving the spatialization cues. One was auditory directional discrimination degree based on semantic differential method, in which the azimuth information of sound sources was told to the listeners before listening. The other was auditory localization, in which the azimuth information was not told to the listeners before listening. The corresponding statistical analyses for the subjective experimental results were made. All the experimental results support that individualized HRTFs obtained from the presented method achieve a preferable performance in perceiving the spatialization cues.

On the Influence of Continuous Subject Rotation During High-Resolution Head-Related Transfer Function Measurements

In recent years, the required time for high resolution individual head-related transfer function (HRTF) measurements has been reduced by the use of microphone or loudspeaker arrays and with parallelization of measurement signals. While a short measurement time is beneficial during HRTF measurement of any resolution, it is essential for the acquisition of high resolution individual HRTF measurements since subjects cannot remain motionless over extended periods of time. This decrease in measurement time can be achieved by using continuous rotation during the measurement, instead of step-wise subject positioning. This paper examines the influence of such continuous measurement rotation with sine sweep measurements and the necessary subsequent post-processing. The goal is to identify a rotation speed, from a predefined set of speeds, that does not have an audible influence on the measured HRTF itself. To this end, a subjective and objective comparison between the two measurement modes is presented in this paper. Five HRTF measurements of an artificial head are acquired using different rotation speeds. They are compared objectively to a reference measurement of the same head performed with step-wise measurement rotation in the same measurement setup. A subjective listening experiment examines whether the found differences between the measurements do result in an audible difference. The objective evaluation showed expected trends regarding the dependency of the error on the measurement speed and is used to validate the used signal post-processing. The subjective evaluation showed that with a rotation of 3.8 $^\circ$ /s no audible differences to the reference are present.

Individualization of head-related transfer functions using sparse representation approach

The individualization of Head-Related Transfer Functions (HRTFs) is an important issue for enhancing the performance of binaural auralization. In this paper, the HRTFs and anthropometric parameters of Chinese people were measured and analyzed. A sparse representation approach was suggested to synthesize individualized HRTFs with selected anthropometric features. The approach was compared with two other methods proposed in previous studies by comparing the spectrum distortion of each method for objective evaluation. Then, subjective experiments were conducted to investigate the performance of the optimized HRTFs in binaural localization. The evaluation results show that the proposed HRTFs individualization approach has smaller spectrum distortion and better localization performance than that of the reference methods.

Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition

Head-related transfer function (HRTF) plays an important role in three-dimensional spatial sound system. However, the direct application of a large amount of original HRTF data would involve a great deal of computational burden, especially for high-spatial-resolution individual HRTF. To address this problem, we propose a novel compression method (called TT-Tucker) combining Tucker model with tensor train decomposition based on a 5-order HRTF tensor model developed in subspaces of an ear, subject, azimuth, elevation, and frequency. Lots of HRTF data can be decomposed into several low-parametric factors representing the key spectrum information of HRTF by capturing the hidden interactions among different subspaces. To evaluate the reconstruction performance, the numerical experiments were conducted on the CIPIC HRTF database. Under the same compression ratio of nearly 98%, the results suggest that the proposed method has a better performance in spectral distortion and signal-to-distortion ratio than that of the usual tensor method and the standard method principal component analysis (PCA). Moreover, the subjective listening test shows that the TT-Tucker method performs better in that, the compressed and reconstructed HRTF is closer to the original HRTF in the sound localization similarity.

Do We Need Individual Head-Related Transfer Functions for Vertical Localization? The Case Study of a Spectral Notch Distance Metric

This paper deals with the issue of individualizing the head-related transfer function (HRTF) rendering process for auditory elevation perception. Is it possible to find a nonindividual, personalized HRTF set that allows a listener to have an equally accurate localization performance than with his/her individual HRTFs? We propose a psychoacoustically motivated, anthropometry based mismatch function between HRTF pairs that exploits the close relation between the listener's pinna geometry and localization cues. This is evaluated using an auditory model that computes a mapping between HRTF spectra and perceived spatial locations. Results on a large number of subjects in the center for image processing and integrated computing (CIPIC) and acoustics research institute (ARI) HRTF databases suggest that there exists a nonindividual HRTF set, which allows a listener to have an equally accurate vertical localization than with individual HRTFs. Furthermore, we find the optimal parameterization of the proposed mismatch function, i.e., the one that best reflects the information given by the auditory model. Our findings show that the selection procedure yields statistically significant improvements with respect to dummy-head HRTFs or random HRTF selection, with potentially high impact from an applicative point of view.

Real-time loudness normalisation with combined cochlear implant and hearing aid stimulation.

BackgroundPeople who use a cochlear implant together with a contralateral hearing aid—so-called bimodal listeners—have poor localisation abilities and sounds are often not balanced in loudness across ears. In order to address the latter, a loudness balancing algorithm was created, which equalises the loudness growth functions for the two ears. The algorithm uses loudness models in order to continuously adjust the two signals to loudness targets. Previous tests demonstrated improved binaural balance, improved localisation, and better speech intelligibility in quiet for soft phonemes. In those studies, however, all stimuli were preprocessed so spontaneous head movements and individual head-related transfer functions were not taken into account. Furthermore, the hearing aid processing was linear.Study designIn the present study, we simplified the acoustical loudness model and implemented the algorithm in a real-time system. We tested bimodal listeners on speech perception and on sound localisation, both in normal loudness growth configuration and in a configuration with a modified loudness growth function. We also used linear and compressive hearing aids.ResultsThe comparison between the original acoustical loudness model and the new simplified model showed loudness differences below 3% for almost all tested speech-like stimuli and levels. We found no effect of balancing the loudness growth across ears for speech perception ability in quiet and in noise. We found some small improvements in localisation performance. Further investigation with a larger sample size is required.

Spatial perception in binaural reproduction with sparse head-related transfer function measurement grids

Growing interest in virtual reality has led to greater demand for immersive virtual audio systems. High fidelity spatial audio requires individualized head related transfer functions (HRTFs). Individualized HRTFs are, however, typically unavailable as they require specialized equipment and a large number of measurements. This motivates the development of a simpler more accessible HRTF estimation process. Previous work has demonstrated that spherical-harmonics (SH) can be used to reconstruct the HRTF from a smaller number of sample points, but this method has two known types of error: spatial aliasing and truncation error. Aliasing refer to loss of ability to represent high frequencies. Truncation error refer to the fact the SH representation will be order-limited which further limits spatial resolution. In this paper, the effect of sparse measurement grids on the reproduced binaural signal is studied by analyzing both types of errors. The expected effect of these errors on the perceived location, externalization and source width, is studied using a theoretical model and an experimental investigation. Preliminary results indicate relatively large effects of SH order on sound localization and smaller effects due to aliasing errors.

Efficient aliasing-free HRTF representation

Previous studies have shown that individualized head related transfer functions (HRTFs) provide improved localization performance compared to generic HRTF filters, and are therefore considered preferable for binaural sound reproduction. However, individualized HRTFs typically require a large number of measurements, which may extend to several hours. Therefore, they are currently unavailable for the vast majority of users. One approach to lower measurement complexity is to reduce the number of HRTF measurements. This approach although simplifies the measurement process, produces spatial aliasing leading to interpolation error away from the measured directions. In this study, a new method for the measurement of individualized HRTFs with a reduced spatial aliasing error is developed. The reduced spatial aliasing error is achieved by incorporating a small number of individualized HRTF measurements with information from a high resolution HRTFs database, which leads to an optimal interpolation process in the se...

The effects of diffuse noise and artificial reverberation on listener weighting of interaural cues in sound localization

The reliability of interaural time and level difference (ITD, ILD) sound location cues can be degraded by noise or reverberation. In this study, we determined how weighting of ITD and ILD varied with signal-to-noise ratio (SNR) in the presence of interaurally uncorrelated background noise and with direct-to-reverberant ratio (DRR) in the presence of artificial reverberation (generated by convolving the target signal with an interaurally uncorrelated pair of impulse responses created by multiplying Gaussian noise with a decaying exponential, RT60 = 500 ms). Wideband (0.5-16 kHz) 100-ms noise-burst targets were presented over headphones using individual head-related transfer functions. ITD and ILD were manipulated by attenuating or delaying the sound at one ear (by up to 300 μs or 10 dB), and cue weighting was computed by comparing localization response bias to imposed cue bias. Wideband (0.5-16 kHz) and low-pass (0.5-2 kHz) noise and reverberation and SNRs and DRRs from -5 to + 20 dB were used. ITD dominat...

On the influence of continuous subject rotation during HRTF measurements

In recent years, the measurement time of individual Head-Related Transfer Function (HRTF) measurements has been reduced by the use of loudspeaker arrays. The time reduction is achieved by some kind of parallelization of measurement signals. One such fast system was developed at the Institute of Technical Acoustics, RWTH Aachen University and is evaluated in this paper. When measuring HRTFs, the subject is usually rotated by some angle, and stops and waits for the measurement signal to complete before moving to the next measurement angle. It was shown that with this static approach a comparable results to a traditional measurement using a single speaker could be achieved. To further reduce the measurement time, a slow continuous subject rotation can be used instead. While this rotation will violate LTI (linear, time-invariant) requirements of the commonly used signal processing, the influence is assumed to be negligible. As the subject is rotating during the measurement sweep, different azimuth angles are measured per frequency. This frequency dependent offset in the measurement positions has to be corrected during the post processing. To this end, a spherical harmonic decomposition and reconstruction is applied as an interpolation method. To quantify the influence of the rotation and the subsequent post processing, a subjective and objective comparison between statically and continuously measured objects is shown in this paper.In recent years, the measurement time of individual Head-Related Transfer Function (HRTF) measurements has been reduced by the use of loudspeaker arrays. The time reduction is achieved by some kind of parallelization of measurement signals. One such fast system was developed at the Institute of Technical Acoustics, RWTH Aachen University and is evaluated in this paper. When measuring HRTFs, the subject is usually rotated by some angle, and stops and waits for the measurement signal to complete before moving to the next measurement angle. It was shown that with this static approach a comparable results to a traditional measurement using a single speaker could be achieved. To further reduce the measurement time, a slow continuous subject rotation can be used instead. While this rotation will violate LTI (linear, time-invariant) requirements of the commonly used signal processing, the influence is assumed to be negligible. As the subject is rotating during the measurement sweep, different azimuth angles are ...

Sound localization by head-related transfer functions: Data sets and issues for individualization of head-related transfer functions

Sound localization by head-related transfer functions: Data sets and issues for individualization of head-related transfer functions