Search

Introduction: Sensorimotor adaptation has often been studied in the visual modality through the Prism Adaptation (PA) paradigm. In this paradigm, a lateral shift in visual pointing was found after wearing prismatic goggles. An effect of PA has sometimes been observed on hearing, in favor of a cross-modality recalibration. However, no study has ever shown if a biased auditory-motor adaptation could induce this lateral shift, which appears essential to a better understanding of the mechanisms of auditory adaptation. The present study aimed at inducing an auditory prism-like effect. Methods: Sixty healthy young adults underwent a session of active audio-proprioceptive training in immersive virtual reality based on Head Related Transfer Functions (HRTF). This training consisted of a game in which the hand-held controller emitted sounds either at its actual position in a control group or at 10° or 20° to the right of its actual position in two experimental groups. Sound localization was assessed before and after the training. Results: The difference between both localization tests was significantly different between the three groups. As expected, the difference was significantly leftward for the group with a 20° deviation compared to the control group. However, this effect is due to a significant rightward deviation in the control group whereas no significant difference between localization tests emerged in the two experimental groups, suggesting that other factors such as fatigue may have cumulated with the training after-effect. Discussion: More studies are needed to determine which angle of deviation and which number of sessions of this audio-proprioceptive training are required to obtain the best after-effect. Although the coupling of hearing and vision in PA still needs to be studied, adding spatial hearing to PA programs could be a promising way to reinforce after-effects and optimize their benefits. [ABSTRACT FROM AUTHOR]

Auditory localization is a fundamental ability that allows to perceive the spatial location of a sound source in the environment. The present work aims to provide a comprehensive overview of the mechanisms and acoustic cues used by the human perceptual system to achieve such accurate auditory localization. Acoustic cues are derived from the physical properties of sound waves, and many factors allow and influence auditory localization abilities. This review presents the monaural and binaural perceptual mechanisms involved in auditory localization in the three dimensions. Besides the main mechanisms of Interaural Time Difference, Interaural Level Difference and Head Related Transfer Function, secondary important elements such as reverberation and motion, are also analyzed. For each mechanism, the perceptual limits of localization abilities are presented. A section is specifically devoted to reference systems in space, and to the pointing methods used in experimental research. Finally, some cases of misperception and auditory illusion are described. More than a simple description of the perceptual mechanisms underlying localization, this paper is intended to provide also practical information available for experiments and work in the auditory field. [ABSTRACT FROM AUTHOR]

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function. In this study, we aimed to investigate the disparities between the perceived locations of sound sources by users and the locations generated by the platform. Our goal was to determine if it is possible to train users to adapt to the platform-generated sound sources. We utilized the Microsoft HoloLens 2 virtual platform and collected data from 12 subjects based on six separate training sessions arranged in 2 weeks. We employed three modes of training to assess their effects on sound localization, in particular for studying the impacts of multimodal error, visual, and sound guidance in combination with kinesthetic/postural guidance, on the effectiveness of the training. We analyzed the collected data in terms of the training effect between pre- and post-sessions as well as the retention effect between two separate sessions based on subject-wise paired statistics. Our findings indicate that, as far as the training effect between pre- and post-sessions is concerned, the effect is proven to be statistically significant, in particular in the case wherein kinesthetic/postural guidance is mixed with visual and sound guidance. Conversely, visual error guidance alone was found to be largely ineffective. On the other hand, as far as the retention effect between two separate sessions is concerned, we could not find any meaningful statistical implication on the effect for all three error guidance modes out of the 2-week session of training. These findings can contribute to the improvement of VR technologies by ensuring they are designed to optimize human sound localization abilities. [ABSTRACT FROM AUTHOR]

IntroductionSensorimotor adaptation has often been studied in the visual modality through the Prism Adaptation (PA) paradigm. In this paradigm, a lateral shift in visual pointing was found after wearing prismatic goggles. An effect of PA has sometimes been observed on hearing, in favor of a cross-modality recalibration. However, no study has ever shown if a biased auditory-motor adaptation could induce this lateral shift, which appears essential to a better understanding of the mechanisms of auditory adaptation. The present study aimed at inducing an auditory prism-like effect.MethodsSixty healthy young adults underwent a session of active audio-proprioceptive training in immersive virtual reality based on Head Related Transfer Functions (HRTF). This training consisted of a game in which the hand-held controller emitted sounds either at its actual position in a control group or at 10° or 20° to the right of its actual position in two experimental groups. Sound localization was assessed before and after the training.ResultsThe difference between both localization tests was significantly different between the three groups. As expected, the difference was significantly leftward for the group with a 20° deviation compared to the control group. However, this effect is due to a significant rightward deviation in the control group whereas no significant difference between localization tests emerged in the two experimental groups, suggesting that other factors such as fatigue may have cumulated with the training after-effect.DiscussionMore studies are needed to determine which angle of deviation and which number of sessions of this audio-proprioceptive training are required to obtain the best after-effect. Although the coupling of hearing and vision in PA still needs to be studied, adding spatial hearing to PA programs could be a promising way to reinforce after-effects and optimize their benefits.

Auditory localization is a fundamental ability that allows to perceive the spatial location of a sound source in the environment. The present work aims to provide a comprehensive overview of the mechanisms and acoustic cues used by the human perceptual system to achieve such accurate auditory localization. Acoustic cues are derived from the physical properties of sound waves, and many factors allow and influence auditory localization abilities. This review presents the monaural and binaural perceptual mechanisms involved in auditory localization in the three dimensions. Besides the main mechanisms of Interaural Time Difference, Interaural Level Difference and Head Related Transfer Function, secondary important elements such as reverberation and motion, are also analyzed. For each mechanism, the perceptual limits of localization abilities are presented. A section is specifically devoted to reference systems in space, and to the pointing methods used in experimental research. Finally, some cases of misperception and auditory illusion are described. More than a simple description of the perceptual mechanisms underlying localization, this paper is intended to provide also practical information available for experiments and work in the auditory field.

This paper presents an innovative approach for personalized modeling of Head-Related Transfer Functions (HRTFs) using deep neural networks (DNNs), with a primary focus on addressing interaural time differences (ITDs), torso and pinna anthropometrics. HRTFs play a pivotal role in spatial hearing applications, such as virtual auditory displays and hearing assistive devices. The core objectives of this study involve mitigating measurement irregularities related to sudden head movements, microphone and sound source positioning, and head orientation. A significant component of this paper introduces a novel ITD correction method designed to reduce irregularities within the HRTF database. This method leverages ITD data, in conjunction with torso and pinna anthropometrics, within a DNN-based model to estimate individual HRTFs for precise spatial directions. Comparisons are made between this approach and traditional head measurement methods. Additionally, this research investigates the sensitivity of HRTFs to minor changes in head position. By analyzing variations in left and right pinna HRTFs at different head tilt angles through numerical simulations, using a 3D model of a human head and torso, the study quantifies spectral distortion in HRTFs. It highlights that spectral distortion generally increases with head motion and sound signal frequency, with a minor decrease observed at 16 kHz. In summary, this paper contributes to the advancement of personalized HRTF modeling, particularly in scenarios involving fluctuations in head position. The findings have the potential to enhance the effectiveness of machine learning algorithms for sound localization, particularly when considering both pinnae HRTFs. This research underscores the importance of these aspects in optimizing spatial hearing experiences.

Sound localization is a crucial aspect of human auditory perception. VR (virtual reality) technologies provide immersive audio platforms that allow human listeners to experience natural sounds based on their ability to localize sound. However, the simulations of sound generated by these platforms, which are based on the general head-related transfer function (HRTF), often lack accuracy in terms of individual sound perception and localization due to significant individual differences in this function. In this study, we aimed to investigate the disparities between the perceived locations of sound sources by users and the locations generated by the platform. Our goal was to determine if it is possible to train users to adapt to the platform-generated sound sources. We utilized the Microsoft HoloLens 2 virtual platform and collected data from 12 subjects based on six separate training sessions arranged in 2 weeks. We employed three modes of training to assess their effects on sound localization, in particular for studying the impacts of multimodal error, visual, and sound guidance in combination with kinesthetic/postural guidance, on the effectiveness of the training. We analyzed the collected data in terms of the training effect between pre- and post-sessions as well as the retention effect between two separate sessions based on subject-wise paired statistics. Our findings indicate that, as far as the training effect between pre- and post-sessions is concerned, the effect is proven to be statistically significant, in particular in the case wherein kinesthetic/postural guidance is mixed with visual and sound guidance. Conversely, visual error guidance alone was found to be largely ineffective. On the other hand, as far as the retention effect between two separate sessions is concerned, we could not find any meaningful statistical implication on the effect for all three error guidance modes out of the 2-week session of training. These findings can contribute to the improvement of VR technologies by ensuring they are designed to optimize human sound localization abilities.

Expressing head-related transfer functions (HRTFs) in the spherical harmonic (SH) domain has been thoroughly studied as a method of obtaining continuity over space. However, HRTFs are functions not only of direction but also of frequency. This paper presents an extension of the SH-based method, utilizing hyperspherical harmonics (HSHs) to obtain an HRTF representation that is continuous over both space and frequency. The application of the HSH approximation results in a relatively small set of coefficients which can be decoded into HRTF values at any direction and frequency. The paper discusses results obtained by applying the method to magnitude spectra extracted from exemplary HRTF measurements. The HRTF representations based on SHs and HSHs exhibit similar reproduction accuracy, with the latter one featuring continuity over both space and frequency and requiring much lower number of coefficients. The developed HSH-based continuous functional model can serve multiple purposes, such as interpolation, compression or parametrization for machine-learning applications.

Expressing head-related transfer functions (HRTFs) in the spherical harmonic (SH) domain has been thoroughly studied as a method of obtaining continuity over space. However, HRTFs are functions not only of direction but also of frequency. This paper presents an extension of the SH-based method, utilizing hyperspherical harmonics (HSHs) to obtain an HRTF representation that is continuous over both space and frequency. The application of the HSH approximation results in a relatively small set of coefficients which can be decoded into HRTF values at any direction and frequency. The paper discusses results obtained by applying the method to magnitude spectra extracted from exemplary HRTF measurements. The HRTF representations based on SHs and HSHs exhibit similar reproduction accuracy, with the latter one featuring continuity over both space and frequency and requiring much lower number of coefficients. The developed HSH-based continuous functional model can serve multiple purposes, such as interpolation, compression or parametrization for machine-learning applications. [ABSTRACT FROM AUTHOR]

Abstract Head-related transfer function (HRTF) individualization can improve the perception of binaural sound. The interaural time difference (ITD) of the HRTF is a relevant cue for sound localization, especially in azimuth. Therefore, individualization of the ITD is likely to result in better sound spatial localization. A study of ITD has been conducted from a perceptual point of view using data from individual HRTF measurements and subjective perceptual tests. Two anthropometric dimensions have been demonstrated in relation to the ITD, predicting the subjective behavior of various subjects in a perceptual test. With this information, a method is proposed to individualize the ITD of a generic HRTF set by adapting it with a scale factor, which is obtained by a linear regression formula dependent on the two previous anthropometric dimensions. The method has been validated with both objective measures and another perceptual test. In addition, practical regression formula coefficients are provided for fitting the ITD of the generic HRTFs of the widely used Brüel & Kjær 4100 and Neumann KU100 binaural dummy heads.

Abstract One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

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.

Headphones-based spatial audio simulations rely on Head-related Transfer Functions (HRTFs) in order to reconstruct the sound field at the entrance of the listener’s ears. A HRTF is strongly dependent on the listener’s specific anatomical structures, and it has been shown that virtual sounds recreated with someone else’s HRTF result in worse localisation accuracy, as well as altering other subjective measures such as externalisation and realism. Acoustic measurements of the filtering effects generated by ears, head and torso has proven to be one of the most reliable ways to obtain a personalised HRTF. However this requires a dedicated and expensive setup, and is time-intensive. In order to simplify the measurement setup, thereby improving the scalability of the process, we are exploring strategies to reduce the number of acoustic measurements without degrading the spatial resolution of the HRTF. Traditionally, spatial up-sampling of HRTF sets is achieved through barycentric interpolation or by employing the spherical harmonics framework. However, such methods often perform poorly when the provided HRTF data is spatially very sparse. This work investigates the use of generative adversarial networks (GANs) to tackle the up-sampling problem, offering an initial insight about the suitability of this technique. Numerical evaluations based on spectral magnitude error and perceptual model outputs are presented on single spatial dimensions, therefore considering sources positioned only in one of the three main planes: Horizontal, median, and frontal. Results suggest that traditional HRTF interpolation methods perform better than the proposed GAN-based one when the distance between measurements is smaller than 90°, but for the sparsest conditions (i.e., one measurement every 120°–180°), the proposed approach outperforms the others.

Head-related transfer function (HRTF) individualization can improve the perception of binaural sound. The interaural time difference (ITD) of the HRTF is a relevant cue for sound localization, especially in azimuth. Therefore, individualization of the ITD is likely to result in better sound spatial localization. A study of ITD has been conducted from a perceptual point of view using data from individual HRTF measurements and subjective perceptual tests. Two anthropometric dimensions have been demonstrated in relation to the ITD, predicting the subjective behavior of various subjects in a perceptual test. With this information, a method is proposed to individualize the ITD of a generic HRTF set by adapting it with a scale factor, which is obtained by a linear regression formula dependent on the two previous anthropometric dimensions. The method has been validated with both objective measures and another perceptual test. In addition, practical regression formula coefficients are provided for fitting the ITD of the generic HRTFs of the widely used Brüel & Kjær 4100 and Neumann KU100 binaural dummy heads. [ABSTRACT FROM AUTHOR]

Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment, and the sensory evidence consists of auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. The model estimates the direction of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. We found that the variant equipped with spectral gradient profiles outperformed other localization models. The proposed model appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach. [ABSTRACT FROM AUTHOR]

The auditory system derives locations of sound sources from spatial cues provided by the interaction of sound with the head and external ears. Those cues are analyzed in specific brainstem pathways and then integrated as cortical representation of locations. The principal cues for horizontal localization are interaural time differences (ITDs) and interaural differences in sound level (ILDs). Vertical and front/back localization rely on spectral-shape cues derived from direction-dependent filtering properties of the external ears. The likely first sites of analysis of these cues are the medial superior olive (MSO) for ITDs, lateral superior olive (LSO) for ILDs, and dorsal cochlear nucleus (DCN) for spectral-shape cues. Localization in distance is much less accurate than that in horizontal and vertical dimensions, and interpretation of the basic cues is influenced by additional factors, including acoustics of the surroundings and familiarity of source spectra and levels. Listeners are quite sensitive to sound motion, but it remains unclear whether that reflects specific motion detection mechanisms or simply detection of changes in static location. Intact auditory cortex is essential for normal sound localization. Cortical representation of sound locations is highly distributed, with no evidence for point-to-point topography. Spatial representation is strictly contralateral in laboratory animals that have been studied, whereas humans show a prominent right-hemisphere dominance.

The auditory system derives locations of sound sources from spatial cues provided by the interaction of sound with the head and external ears. Those cues are analyzed in specific brainstem pathways and then integrated as cortical representation of locations. The principal cues for horizontal localization are interaural time differences (ITDs) and interaural differences in sound level (ILDs). Vertical and front/back localization rely on spectral-shape cues derived from direction-dependent filtering properties of the external ears. The likely first sites of analysis of these cues are the medial superior olive (MSO) for ITDs, lateral superior olive (LSO) for ILDs, and dorsal cochlear nucleus (DCN) for spectral-shape cues. Localization in distance is much less accurate than that in horizontal and vertical dimensions, and interpretation of the basic cues is influenced by additional factors, including acoustics of the surroundings and familiarity of source spectra and levels. Listeners are quite sensitive to sound motion, but it remains unclear whether that reflects specific motion detection mechanisms or simply detection of changes in static location. Intact auditory cortex is essential for normal sound localization. Cortical representation of sound locations is highly distributed, with no evidence for point-to-point topography. Spatial representation is strictly contralateral in laboratory animals that have been studied, whereas humans show a prominent right-hemisphere dominance.

Abstract In response to renewed interest in virtual and augmented reality, the need for high-quality spatial audio systems has emerged. The reproduction of immersive and realistic virtual sound requires high resolution individualized head-related transfer function (HRTF) sets. In order to acquire an individualized HRTF, a large number of spatial measurements are needed. However, such a measurement process requires expensive and specialized equipment, which motivates the use of sparsely measured HRTFs. Previous studies have demonstrated that spherical harmonics (SH) can be used to reconstruct the HRTFs from a relatively small number of spatial samples, but reducing the number of samples may produce spatial aliasing error. Furthermore, by measuring the HRTF on a sparse grid the SH representation will be order-limited, leading to constrained spatial resolution. In this paper, the effect of sparse measurement grids on the reproduced binaural signal is studied by analyzing both aliasing and truncation errors. The expected effect of these errors on the perceived loudness stability of the virtual sound source is studied theoretically, as well as perceptually by an experimental investigation. Results indicate a substantial effect of truncation error on the loudness stability, while the added aliasing seems to significantly reduce this effect.

Una onda sonora es transformada por el torso, la cabeza y las orejas, antes de alcanzar los tímpanos de un oyente. Estas transformaciones son diferentes para cada oído, ángulo de incidencia y varían de persona a persona. Las respuestas impulsivas de cabeza (HRIRs) en el dominio del tiempo, o las funciones de transferencia de cabeza (HRTFs) en el dominio de la frecuencia, caracterizan la transmisión entre una fuente sonora y los tímpanos de una persona. En este trabajo se presenta el desarrollo de un sistema de medición de HRIRs, que utiliza como señal de excitación códigos de Golay. Estas secuencias binarias brindan buenos resultados con ruido de fondo elevado y crea respuestas impulsivas sin los ruidos espurios y modulaciones que suelen incorporar los barridos sinusoidales en estas condiciones. Los resultados obtenidos en mediciones a un simulador de cabeza y torso son coherentes con estudios similares. Se analizan además las principales características de los perfiles espectrales de las HRTFs medidas, que resultan en indicios para localizar una fuente sonora.

A virtual‐sound catch ball game system was developed to improve the quality of life of the visually impaired. Computer‐generated virtual sound balls were represented by stereo sounds, comprising localization parameters such as the interaural time and level differences, echo, and head‐related transfer function. Ball‐catching success was determined by images of the hand captured by a USB camera. Two sessions of experiments with blind subjects demonstrated the importance of distance recognition for human localization capability. Moreover, the addition of an echo parameter enhanced the sound‐source image position and sense of enjoyment. The results showed that a virtual sound game is a good active‐entertainment option for the visually impaired. [ABSTRACT FROM AUTHOR]

A neural code adapted to the statistical structure of sensory cues may optimize perception. We investigated whether interaural time difference (ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminability. The natural ITD rate of change across azimuth (ITDrc) and ITD variability over time (ITDv) were combined in a Fisher information statistic to assess the amount of azimuthal information conveyed by this sensory cue. We hypothesized that natural ITD statistics underlie the neural code for ITD and thus influence spatial perception. To test this hypothesis, sounds with invariant statistics were presented to measure human spatial discriminability and spatial novelty detection. Human auditory spatial perception showed correlation with natural ITD statistics, supporting our hypothesis. Further analysis showed that these results are consistent with classic models of ITD coding and can explain the ITD tuning distribution observed in the mammalian brainstem.

Head-Related Transfer Function (HRTF) is the most important factor to achieve Auditory Space Modeling(ASM). HRTF has many applications in the areas of room acoustic modeling, spatial hearing and multimedia. For the reason that HRTF is related to the location and frequency of sound source and the physiological structures of the listener, customization of HRTF becomes the bottle-neck problem of the research and application of ASM. This paper, based on the measured data offers the personalization method of the HRTF based on the multi-dimensional physiological parameters, and the method is verified through the spectral distortion and the subjective auditory localization experiments. The results show that database matching can obtain the personalized HRTF efficiently and in the spatial hearing experiment the personalized HRTF can reduce the front-back confusion of auditory localization and improve the accuracy.

We propose a new method to improve median plane sound localization performance using a nonlinear representation of head-related transfer functions (HRTFs) and a recommender system. First, we reduce the dimensionality of an HRTF data set with multiple subjects using manifold learning in conjunction with a customized intersubject graph which takes into account relevant prior knowledge of HRTFs. Then, we use a sound localization model to estimate a subject's localization performance in terms of polar error and quadrant error rate. These metrics are merged to form a single rating per HRTF pair that we feed into a recommender system. Finally, the recommender system takes the low-dimensional HRTF representation as well as the ratings obtained from the localization model to predict the best HRTF set, possibly constructed by mixing HRTFs from different individuals, that minimizes a subject's localization error. The simulation results show that our method is capable of choosing a set of HRTFs that improves the median plane localization performance with respect to the mean localization performance using non-individualized HRTFs. Moreover, the localization performance achieved by our HRTF recommender system shows no significant difference to the localization performance observed with the best matching non-individualized HRTFs but with the advantage of not having to perform listening tests with all individuals' HRTFs from the database.

The goal of this work is to compare the quality of replicas of ears (pinnae) for acoustic purposes. We present a method that creates replicas of ears used in acoustic instrumentation, from dummy heads, based on a process of negative molds of alginate and plaster positive molds. The quality of the process is verified by two methods; i) physical and ii) acoustic, the first consisting of visual inspection and comparison of distances and lengths of the original and the replicas and the second in comparative acoustic measurements. The results show that the mold methods deliver results of very good quality and that it allows to use it for acoustic applications. [ABSTRACT FROM AUTHOR]

• The model is capable of concurrently localizing multiple sound sources in azimuth with minimum computational costs, which makes it suitable for implementations in spatial hearing aids. • A unique normalization procedure on the binaural amplitude and phase creates spiral patterns that change systematically with sound frequencies. • The model is invariant with sound profiles. • The model can explain localization behaviors under various types of room reverberations. The human auditory system can localize multiple sound sources using time, intensity, and frequency cues in the sound received by the two ears. Being able to spatially segregate the sources helps perception in a challenging condition when multiple sounds coexist. This study used model simulations to explore an algorithm for localizing multiple sources in azimuth with binaural (i.e., two) microphones. The algorithm relies on the "sparseness" property of daily signals in the time-frequency domain, and sound coming from different locations carrying unique spatial features will form clusters. Based on an interaural normalization procedure, the model generated spiral patterns for sound sources in the frontal hemifield. The model itself was created using broadband noise for better accuracy, because speech typically has sporadic energy at high frequencies. The model at an arbitrary frequency can be used to predict locations of speech and music that occurred alone or concurrently, and a classification algorithm was applied to measure the localization error. Under anechoic conditions, averaged errors in azimuth increased from 4.5° to 19° with RMS errors ranging from 6.4° to 26.7° as model frequency increased from 300 to 3000 Hz. The low-frequency model performance using short speech sound was notably better than the generalized cross-correlation model. Two types of room reverberations were then introduced to simulate difficult listening conditions. Model performance under reverberation was more resilient at low frequencies than at high frequencies. Overall, our study presented a spiral model for rapidly predicting horizontal locations of concurrent sound that is suitable for real-world scenarios. [ABSTRACT FROM AUTHOR]

Utilizing spherical harmonic (SH) domain has been established as the default method of obtaining continuity over space in head-related transfer functions (HRTFs). This paper concerns different variants of extending this solution by replacing SHs with four-dimensional (4D) continuous functional models in which frequency is imagined as another physical dimension. Recently developed hyperspherical harmonic (HSH) representation is compared with models defined in spherindrical coordinate system by merging SHs with one-dimensional basis functions. The efficiency of both approaches is evaluated based on the reproduction errors for individual HRTFs from HUTUBS database, including detailed analysis of its dependency on chosen orders of approximation in frequency and space. Employing continuous functional models defined in 4D coordinate systems allows HRTF magnitude spectra to be expressed as a small set of coefficients which can be decoded back into values at any direction and frequency. The best performance was noted for HSHs and SHs merged with reverse Fourier–Bessel series, with the former featuring better compression abilities, achieving slightly higher accuracy for low number of coefficients. The presented models can serve multiple purposes, such as interpolation, compression or parametrization for machine learning applications, and can be applied not only to HRTFs but also to other types of directivity functions, e.g. sound source directivity. • Hyperspherical harmonics are the most efficient 4D basis for storing HRTFs. • Frequency approximation order should be higher than maximum spherical harmonic order. • Utilizing 4D HRTF representations allows for a massive reduction of data size. • HRTF model evaluations are stable for different subjects from the same databases. [ABSTRACT FROM AUTHOR]

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.

This study examines the efficiency of a training protocol using a virtual reality application designed to accelerate individual’s selection of, and accommodation to, non-individualized HRTF profiles. This training introduces three elements to hasten audio localization performance improvement: an interactive HRTF selection method, a parametric training program based on active learning, and a relatively dry room acoustic simulation designed to increase the quantity of spatial cues presented. Participants rapidly selected an HRTF (≈5 min) followed by training over three sessions of 12 min distributed over 5 days. To study the impact of the room acoustic component on localization performance evolution, participants were divided into two groups: one acting as control reference, training with only anechoic renderings, the other training in reverberant conditions. The efficiency of the training program was assessed across groups and the entire protocol was assessed through direct comparisons with results reported in previous studies. Results indicate that the proposed training program led to improved learning rates compared to that of previous studies, and that the included room response accelerated the learning process.

Headphone transfer function (HpTF) and head-related transfer function (HRTF) measurements are crucial in acoustic science and in binaural virtual acoustic applications. Yet, their measurement set-up, procedure or post-processing is different for nearly every lab, especially for the HRTF measurements. To compare findings between different labs, these measurement deviations have to be quantified alongside with their influence on perceptual aspects. In the scope of a cross-site investigation on loudness balancing between headphone and loudspeaker listening, a set of HpTFs with three different headphones (open, closed, insert earphones) and HRTF close to the eardrum were measured in 14 participants travelling to two different measurement sites at Aachen and Oldenburg. Though set-ups for measuring the HRTF are very different between sites, the gathered HRTFs are quite consistent across them. For the measured HpTFs, across sites the open headphones consistently yield a slightly lower variability in the range from 70 to 5000 Hz than the closed one while the insert earphones exhibit much higher variabilities and a limited range of reproducible results. The difference in loudness balancing across labs could well be predicted by site-specific systematic differences in HpTFs with the exception of 1 kHz narrowband stimulus. This clearly indicates the limits in comparability of HpTFs and loudness balancing across labs and the importance of using headphones with high repeatability like the open ones used in this investigation.

The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed. [ABSTRACT FROM AUTHOR]

In a tele-operated robot environment, reproducing auditory scenes and conveying 3D spatial information of sound sources are inevitable in order to make operators feel more realistic tele-presence. In this paper, we propose a tele-presence robot system that enables reproduction and manipulation of auditory scenes. This tele-presence system is carried out on the basis of 3D information about where targeted humans are speaking, and matching with the operator's head orientation. We employed multiple microphone arrays and human tracking technologies to localize and separate voices around a robot. In the operator site, separated sound sources are rendered using head-related transfer functions (HRTF) according to the sound sources' spatial positions and the operator's head orientation that is being tracked real time. Two-party and three-party interaction experiments indicated that the proposed system has significantly higher accuracy when perceiving direction of sounds and gains higher subjective scores in sense of presence and listenability, compared to a baseline system which uses stereo binaural sounds obtained by two microphones mounted on the humanoid robot's ears. [ABSTRACT FROM AUTHOR]

Given an input sound signal and a target virtual sound source, sound spatialisation algorithms manipulate the signal so that a listener perceives it as though it were emitted from the target source. There exist several established spatialisation approaches that deliver satisfactory results when loudspeakers are used to playback the manipulated signal. As headphones have a number of desirable characteristics over loudspeakers, such as portability, isolation from the surrounding environment, cost and ease of use, it is interesting to explore how a sense of acoustic space can be conveyed through them. This article first surveys traditional spatialisation approaches intended for loudspeakers, and then reviews them with regard to their adaptability to headphones. [ABSTRACT FROM AUTHOR]