The Effect of Head-Related Transfer Function Measurement Methodology on Localization Performance in Spatial Audio Interfaces

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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).

The KEMAR Head and Torso Simulator is one of the most commonly used artificial head and torso measurement systems and its head-related transfer function (HRTF) is often used in 3D-Audio applications. As part of this publication, an HRTF measurement performed in an hemi-anechoic chamber as well as a high resolution 3D-Model of the same is provided. This accompanying report details the data acquisition and post-processing steps, that resulted in the provided data sets including a discussion of the shortcomings of the available measurement setup.

This paper introduces a continuous functional model for head-related transfer functions (HRTFs) in the horizontal auditory scene. The approach uses a separable representation consisting of a Fourier-Bessel series expansion for the spectral components and a conventional Fourier series expansion for the spatial components. Being independent of the data, these two sets of basis functions remain unchanged for all subjects and measurement setups. Hence, the model can transform an individualized HRTF to a subject specific set of coefficients. A continuous functional model is also developed in the time domain. We show the efficient model performance in approximating experimental measurements by using the HRTF measurements from a KEMAR manikin and the synthetic data from the spherical head model. The statistical results are determined from a 50-subject HRTF data set. We also corroborate the predictive capability of the proposed model. The model has near optimal performance, which can be ascertained by comparison with the standard principle component analysis (PCA) and discrete Karhunen-Loeve expansion (KLE) methods at the measurement points and for a given number of parameters.

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.

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.

3-dimensional sound field with high accuracy is an important topic for MR (Mixed Reality) to create the high immersion sense. HRTF (Head-Related Transfer Function) can achieve 3-dimensional sound field, whereas the measurement of the HRTF for each person is difficult because the measurement of the HRTF requires the expensive equipment and environment. In this paper, we propose a method to design HRTF by piecing several HRTFs selected from a large database of HRTFs. As a result of evaluation experiments, we confirmed that the proposed method can design the useful HRTF.

Head-Related Transfer Function (HRTF) representation and interpolation is an important problem in spatial audio. We present a kernel regression method based on Gaussian process (GP) modeling of the joint spatial-frequency relationship between HRTF measurements and obtain a smooth non-linear representation based on data measured over both arbitrary and structured spherical measurement grids. This representation is further extended to the problem of extracting spectral extrema (notches and peaks). We perform HRTF interpolation and spectral extrema extraction using freely available CIPIC HRTF data. Experimental results are shown.

The inferior colliculus (IC) is a midbrain structure containing a multitude of sensory related afferent (input) connections. In particular, there are ascending auditory inputs from both the lateral superior olive (LSO) and the medial superior olive (MSO) brainstem complexes representing binaural (both ears) amplitude difference and time difference of arrival signals respectively. There are also efferent (output) connections directly from the IC to the deeper layers of the superior colliculus (SC). The SC is involved in the generation of ocular motor signals for saccadic (quick, high velocity, involuntary) eye movement. This neurobiological evidence, along with a generalized Hough transform like approach for interpreting quantitative head related transfer function (HRTF) data, are; utilized in creating a model for IC auditory binaural processing for the purpose of sound localization. It is important to note we are not attempting an exact neurobiological equivalent function. We are using anatomical knowledge as a motivation for our model architecture.

While high-fidelity spatial auditory displays require individualized head-related transfer functions (HRTFs), much of the physical structure contained within an HRTF is similar across most individuals. This suggests that a Bayesian estimation technique, which uses sample observations to bias an a priori model towards an individual, may provide benefits in terms of efficiency. Therefore, the current work proposes a Bayesian HRTF framework that utilizes HRTFs in the form of their real spherical harmonic representation. When combined with assumptions of normality, the resulting technique is shown to enable the accurate estimation of an individualized HRTF from a small set of spatially distributed measurements. Moreover, the model provides a convenient way to analyze which components of an HRTF vary most across individuals, and can therefore be used to create very efficient HRTF measurement strategies. A perceptual localization test confirmed that similar localization performance could be attained with an HRTF estimated from as few as 12 spatial measurements, even when confined to the median-sagittal plane.

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.

Head-related transfer functions (HRTFs) vary with both frequency and source position. The near-field HRTFs with source distance less than 1.0 m are particularly complicated due to their distance dependence. Principal component analysis (PCA), which is conventionally carried out in the frequency or time domain, has been widely applied to reduce the dimensionality of far-field HRTF data with source distance greater than 1.0 m. The present work firstly extends the conventional PCA, and then proposes a spatial PCA method in the spatial domain rather than in the frequency or time domain to reduce the dimensionality of near-field HRTF data. An illustrative case indicates that near-field HRTF magnitudes at 9 distances with 493 directions for each distance can be approximately represented by the weighted sum of 15 spectral shape basis functions using the conventional PCA or the weighted sum of 15 spatial basis functions using the spatial PCA. Both representations account for more than 98 % energy variation of the original data, and reduce the dimensionality of the original data to about a quarter. The proposed method is also applicable to the head-related impulse responses in the time domain. Furthermore, the spatial PCA scheme is potentially applicable to simplify near-field HRTF measurement.

The work quantifies the anthropometric dependency of the head-related transfer functions(HRTFs). The human body modifies the soundwaves while traveling to the ear canal from different spatial locations, and the HRTF defines the transformation. The peaks and notches in the HRTF magnitude spectrum, generated by the complex asymmetrical shape of the outer ear (pinna), are essential for sound localization in the elevation plane. However, the anthropometric dependency of HRTF is not well quantified in the literature. The huge experimental cost of conventional HRTF measurement methods is a hurdle in these studies. To overcome this, distance-dependent HRTFs have been evaluated using the finite element-based computational tool on surface meshes of the human upper body with different pinnae. The size and interaural position of the pinna were altered, and various HRTF simulations were carried out in the study. The frequency shift of the spectral peaks and notches in the magnitude spectrum was examined to understand its anthropometric dependency. The dependency of HRTF on human anthropometric features was appraised in the proposed work. It can be beneficial in predicting personalized HRTF according to the size and interaural position of the subject’s pinna.

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.

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