Individual Transfer Functions Research Articles

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

Individual head-related transfer functions (HRTFs) are critical for binaural spatial audio rendering. In contrast to anthropometric parameters and pinnae images, 3D meshes allow for a more direct and comprehensive representation of the anthropometric structure, which provides highly effective inputs for modeling individualized HRTFs. This paper presents a neural network-based method for predicting individualized HRTFs in full space based on 3D meshes. Unlike many previous methods that estimate HRTF spectra at sampling grids or frequencies separately, the proposed model predicts the HRTF spectra of each vertical plane by considering the spectral correlation and continuity across adjacent sampling grids and frequencies. Evaluation results indicate that the proposed method enhances the prominence of peaks and notches in the obtained HRTF spectra and improves the speed and accuracy of HRTF individualization. The log spectral distortion of the proposed method is lower than that of state-of-the-art methods using anthropometric parameters and pinnae images. Further evaluation confirms that the proposed method requires significantly fewer points in 3D meshes when compared to numerical simulation methods. The evaluation based on localization models demonstrates that the HRTFs predicted by the proposed method are perceptually similar to the measured HRTFs.

Modeling of Individual Head-Related Transfer Functions (HRTFs) Based on Spatiotemporal and Anthropometric Features Using Deep Neural Networks

Modeling of Individual Head-Related Transfer Functions (HRTFs) Based on Spatiotemporal and Anthropometric Features Using Deep Neural Networks

Spatial release from masking in the median plane with non-native speakers using individual and mannequin head related transfer functions.

Spatial release from masking (SRM) in speech-on-speech tasks has been widely studied in the horizontal plane, where interaural cues play a fundamental role. Several studies have also observed SRM for sources located in the median plane, where (monaural) spectral cues are more important. However, a relatively unexplored research question concerns the impact of head-related transfer function (HRTF) personalisation on SRM, for example, whether using individually-measured HRTFs results in better performance if compared with the use of mannequin HRTFs. This study compares SRM in the median plane in a speech-on-speech virtual task rendered using both individual and mannequin HRTFs. SRM is obtained using English sentences with non-native English speakers. Our participants show lower SRM performances compared to those found by others using native English participants. Furthermore, SRM is significantly larger when the source is spatialised using the individual HRTF, and this effect is more marked for those with lower English proficiency. Further analyses using a spectral distortion metric and the estimation of the better-ear effect, show that the observed SRM can only partially be explained by HRTF-specific factors and that the effect of the familiarity with individual spatial cues is likely to be the most significant element driving these results.

Standardized assessment of vascular reconstruction kernels in photon-counting CT angiographies of the leg using a continuous extracorporeal perfusion model

This study evaluated the influence of different vascular reconstruction kernels on the image quality of CT angiographies of the lower extremity runoff using a 1st-generation photon-counting-detector CT (PCD-CT) compared with dose-matched examinations on a 3rd-generation energy-integrating-detector CT (EID-CT). Inducing continuous extracorporeal perfusion in a human cadaveric model, we performed CT angiographies of eight upper leg arterial runoffs with radiation dose-equivalent 120 kVp acquisition protocols (CTDIvol 5 mGy). Reconstructions were executed with different vascular kernels, matching the individual modulation transfer functions between scanners. Signal-to-noise-ratios (SNR) and contrast-to-noise-ratios (CNR) were computed to assess objective image quality. Six radiologists evaluated image quality subjectively using a forced-choice pairwise comparison tool. Interrater agreement was determined by calculating Kendall’s concordance coefficient (W). The intraluminal attenuation of PCD-CT images was significantly higher than of EID-CT (414.7 ± 27.3 HU vs. 329.3 ± 24.5 HU; p < 0.001). Using comparable kernels, image noise with PCD-CT was significantly lower than with EID-CT (p ≤ 0.044). Correspondingly, SNR and CNR were approximately twofold higher for PCD-CT (p < 0.001). Increasing the spatial frequency for PCD-CT reconstructions by one level resulted in similar metrics compared to EID-CT (CNRfat; EID-CT Bv49: 21.7 ± 3.7 versus PCD-CT Bv60: 21.4 ± 3.5). Overall image quality of PCD-CTA achieved ratings superior to EID-CTA irrespective of the used reconstruction kernels (best: PCD-CT Bv60; worst: EID-CT Bv40; p < 0.001). Interrater agreement was good (W = 0.78). Concluding, PCD-CT offers superior intraluminal attenuation, SNR, and CNR compared to EID-CT in angiographies of the upper leg arterial runoff. Combined with improved subjective image quality, PCD-CT facilitates the use of sharper convolution kernels and ultimately bears the potential of improved vascular structure assessability.

Numerical discreteness errors in multispecies cosmological N-body simulations

ABSTRACT We present a detailed analysis of numerical discreteness errors in two-species, gravity-only, cosmological simulations using the density power spectrum as a diagnostic probe. In a simple set-up where both species are initialized with the same total matter transfer function, biased growth of power forms on small scales when the solver force resolution is finer than the mean interparticle separation. The artificial bias is more severe when individual density and velocity transfer functions are applied. In particular, significant large-scale offsets in power are measured between simulations with conventional offset grid initial conditions when compared against converged high-resolution results where the force resolution scale is matched to the interparticle separation. These offsets persist even when the cosmology is chosen so that the two particle species have the same mass, indicating that the error is sourced from discreteness in the total matter field as opposed to unequal particle mass. We further investigate two mitigation strategies to address discreteness errors: the frozen potential method and softened interspecies short-range forces. The former evolves particles under the approximately ‘frozen’ total matter potential in linear theory at early times, while the latter filters cross-species gravitational interactions on small scales in low-density regions. By modelling closer to the continuum limit, both mitigation strategies demonstrate considerable reductions in large-scale power spectrum offsets.

The Accuracy of Dynamic Sound Source Localization and Recognition Ability of Individual Head-Related Transfer Functions in Binaural Audio Systems with Head Tracking

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.

Theoretical and Algorithmic Study of Inverses of Arbitrary High-Dimensional Multi-Input Multi-Output Linear-Time-Invariant Systems

Nowadays, systems need to be built and/or characterized to handle exceptional circumstances or adapt to a world itself more complex. A typical phenomenon can often be found that systems are required to accommodate high-dimensional inputs and outputs. Although the theories and methods for inverting a single-input single-output (SISO) linear-time-invariant (LTI) system have been well established, the generalized framework (consisting of theories and algorithms) for extending to arbitrary high-dimensional multi-input multi-output (MIMO) scenarios is still unsubstantial in the existing literature as this extension is far from trivial. In this work, we would like to develop such a new framework for governing the inversion of arbitrary high-dimensional discrete-time MIMO LTI systems, where any individual transfer function from a certain input to a certain output may have the infinite-impulse-response (IIR) characteristics. We propose two new inversion algorithms to invert the transfer-function tensors (TFTs) of arbitrary MIMO LTI systems. The pertinent computational complexities are also investigated for our proposed two TFT-inversion algorithms. The approximation of the inverse of an arbitrary TFT by a finite-impulse-response (FIR) TFT is studied and the corresponding approximation-error analysis is derived as well. Finally, numerical evaluations are presented to study the computational complexities with respect to different TFT dimensions and ranks.

Modeling individual head-related transfer functions from sparse measurements using a convolutional neural network.

Individual head-related transfer functions (HRTFs) are usually measured with high spatial resolution or modeled with anthropometric parameters. This study proposed an HRTF individualization method using only spatially sparse measurements using a convolutional neural network (CNN). The HRTFs were represented by two-dimensional images, in which the horizontal and vertical ordinates indicated direction and frequency, respectively. The CNN was trained by using the HRTF images measured at specific sparse directions as input and using the corresponding images with a high spatial resolution as output in a prior HRTF database. The HRTFs of a new subject can be recovered by the trained CNN with the sparsely measured HRTFs. Objective experiments showed that, when using 23 directions to recover individual HRTFs at 1250 directions, the spectral distortion (SD) is around 4.4 dB; when using 105 directions, the SD reduced to around 3.8 dB. Subjective experiments showed that the individualized HRTFs recovered from 105 directions had smaller discrimination proportion than the baseline method and were perceptually undistinguishable in many directions. This method combines the spectral and spatial characteristics of HRTF for individualization, which has potential for improving virtual reality experience.

Analysis of laser scanning and photogrammetric scanning accuracy on the numerical determination of Head-Related Transfer Functions of a dummy head

Individual Head-Related Transfer Functions (HRTFs) are necessary for the accurate rendering of virtual scenes. However, their acquisition is challenging given the complex pinna shape. Numerical methods can be leveraged to compute HRTFs on meshes originating from precise scans of a subject. Although photogrammetry can be used for the scanning, its inaccuracy might affect the spatial cues of simulated HRTFs. This paper aims to assess the significance of the photogrammetric error affecting a Neumann KU100 dummy head scan. The geometrical differences between the photogrammetric scan and a laser scan are mainly located at the pinna cavities. The computed photogrammetric HRTFs, compared to measured and simulated data using objective and perceptually inspired metrics, show deviation in high frequency spectral features, stemming from the photogrammetric scanning error. This spectral deviation hinders the modelled elevation perception with photogrammetric HRTFs to levels comparable to renderings with nonindividual data. Extracting the photogrammetric geometry at individual ear cavities and merging it to the laser mesh, an assessment of the influence of the inaccuracy at different pinna structures is conducted. Correlation analysis between acoustic and geometrical metrics computed on the results is used to identify the most relevant geometrical metrics in relation to the HRTFs.

Decentralized Relay Feedback Identification of TITO systems

The article focuses on the identification of systems with two inputs and two outputs (TITO systems) where strong cross-couplings exist. The transfer functions of all links of TITO systems are determined from the frequency response points obtained from the time courses of input and output quantities during decentralized relay control. The proposed procedure uses the so-called shifting method, which allows to obtain multiple points of the frequency characteristic from one experiment, thus allowing the estimation of linear models with multiple parameters. In addition, the article proposes a new procedure using the shifting method to obtain the steady state gains of individual transfer functions. The application of this procedure is shown on the instance of a methanol-ethanol distillation column.

Estimation of the Optimal Spherical Harmonics Order for the Interpolation of Head-Related Transfer Functions Sampled on Sparse Irregular Grids

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.

Simultaneous adaption of the gain and phase of a generalized transfer function for aortic pressure waveform estimation

GoalThis paper proposes and validates a completely adaptive transfer function (CATF) based on an autoregressive exogenous (ARX) model which adjusts the gain and phase of a generalized transfer function (GTF) simultaneously to estimate the aortic pressure waveform from a brachial pressure waveform. MethodsInvasive aortic and brachial pressure waveforms were recorded from 34 subjects for the validation of the proposed method. Individual transfer functions (ITFs) were trained based on the pressure waveforms using an ARX model. The GTF was derived by averaging the ITFs. CATF was then obtained by adjusting both the gain and phase of the GTF using regression formulas calculated from the ITFs and brachial hemodynamic parameters. Meanwhile the quantitative contributions of the adaption of gain and phase of the GTF were investigated respectively. The root-mean-square-error of the total waveform and absolute errors of common hemodynamic indices including systolic and diastolic blood pressures (SBP and DBP, respectively), pulse pressure (PP) and augmentation index were used to evaluate the performance of the proposed method in the data divided into low, middle and high PP amplification groups. ResultsThe CATF achieved lower errors for DBP and PP in the low PP amplification group (1.79 versus 2.10 mmHg and 5.08 versus 6.23 mmHg, respectively, both P < 0.05) and PP in the middle amplification group (1.43 versus 1.92 mmHg, P < 0.05) compared with the GTF. SignificanceThe proposed method provides a step towards the development of an improved and clinically useful non-invasive approach for estimating the aortic pressure waveform from a peripheral pressure waveform.

Can I trust my ears in VR? Head-related transfer functions and valuation methods with descriptive attributes in virtual reality

In this article, we present the current state of the art in binaural audio with the focus on head-related transfer functions (HRTFs) and valuation methods of virtual acoustics with descriptive attributes. This combination provides a methodology, which delivers the basis for research studies in virtual reality (VR) on individual and non-individual head-related transfer functions. Based on the largely explored localization perception of static audio signals, this review offers an overview of the directional hearing during head and sound source movement and multimodality in audiovisual virtual environments. Perceptual quality characteristics provide evaluation methods from which future HRTF VR experiments and virtual environments studies on binaural acoustics could benefit.

Dynamic Binaural Rendering: The Advantage of Virtual Artificial Heads over Conventional Ones for Localization with Speech Signals

As an alternative to conventional artificial heads, a virtual artificial head (VAH), i.e., a microphone array-based filter-and-sum beamformer, can be used to create binaural renderings of spatial sound fields. In contrast to conventional artificial heads, a VAH enables one to individualize the binaural renderings and to incorporate head tracking. This can be achieved by applying complex-valued spectral weights—calculated using individual head related transfer functions (HRTFs) for each listener and for different head orientations—to the microphone signals of the VAH. In this study, these spectral weights were applied to measured room impulse responses in an anechoic room to synthesize individual binaural room impulse responses (BRIRs). In the first part of the paper, the results of localizing virtual sources generated with individually synthesized BRIRs and measured BRIRs using a conventional artificial head, for different head orientations, were assessed in comparison with real sources. Convincing localization performances could be achieved for virtual sources generated with both individually synthesized and measured non-individual BRIRs with respect to azimuth and externalization. In the second part of the paper, the results of localizing virtual sources were compared in two listening tests, with and without head tracking. The positive effect of head tracking on the virtual source localization performance confirmed a major advantage of the VAH over conventional artificial heads.

A single-step identification strategy for the coupled TITO process using fractional calculus

The reliable performance of a complete control system depends on accurate model information being used to represent each subsystem. The identification and modelling of multivariable systems are complex and challenging due to cross-coupling. Such a system may require multiple steps and decentralized testing to obtain full system models effectively. In this paper, a direct identification strategy is proposed for the coupled two-input two-output (TITO) system with measurable input–output signals. A well-known closed-loop relay test is utilized to generate a set of inputs–outputs data from a single run. Based on the collected data, four individual fractional-order transfer functions, two for main paths and two for cross-paths, are estimated from single-run test signals. The orthogonal series-based algebraic approach is adopted, namely the Haar wavelet operational matrix, to handle the fractional derivatives of the signal in a simple manner. A single-step strategy yields faster identification with accurate estimation. The simulation and experimental studies depict the efficiency and applicability of the proposed identification technique. The demonstrated results on the twin rotor multiple-input multiple-output (MIMO) system (TRMS) clearly reveal that the presented idea works well with the highly coupled system even in the presence of measurement noise.

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

The Hearpiece database of individual transfer functions of an in-the-ear earpiece for hearing device research

We present a database of acoustic transfer functions of the Hearpiece, a commercially available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human participants and a KEMAR with anthropometric pinnae for five insertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between participants and insertions. Also, the KEMAR measurements are close to the median of human data in the present results for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications. It is openly available at https://doi.org/10.5281/zenodo.3733190.

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.

Analysis of various transfer functions for binary owl search algorithm in feature selection problem

ABSTRACT Owl Search Algorithm (OSA) is a recently proposed nature-inspired meta-heuristic algorithm which is easily implementable and exhibits great potential for solving continuous optimization problems. In our earlier work, a binary version of owl search algorithm (BOSA), with transfer functions for mapping the continuous solution space into a binary one, has been developed and applied in optimal feature subset selection problem. In our preliminary simulation experiments, it was found that the performance of the solution depends on the type of transfer function used. In this work, an extensive analysis of various types of transfer functions and their respective effects on the selection of optimal feature subset has been studied by simulation experiments with multiple benchmark datasets. Transfer functions of three different families, S-shaped, V-shaped and quadratic, are used for designing eleven BOSA models, each of which is made by combining individual transfer function. The performances of the proposed wrapper based feature subset selection algorithm based on several BOSA models have been evaluated by simulation experiments with twenty datasets for finding out the best model. The best observed BOSA model has also been compared with other similar meta-heuristics algorithms for feature subset selection. Experimental results show that the feature subset selected by BOSA with quadratic transfer function produces the highest classification accuracy with the minimum number of selected features compared to other algorithms.