Relevant bibliographies by topics / HRTF individualization

Academic literature on the topic 'HRTF individualization'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "HRTF individualization"

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Jiang, Ziran, Jinqiu Sang, Chengshi Zheng, Andong Li, and Xiaodong Li. "Modeling individual head-related transfer functions from sparse measurements using a convolutional neural network." Journal of the Acoustical Society of America 153, no. 1 (January 2023): 248–59. http://dx.doi.org/10.1121/10.0016854.

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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.
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Braren, Hark Simon, and Janina Fels. "Towards Child-Appropriate Virtual Acoustic Environments: A Database of High-Resolution HRTF Measurements and 3D-Scans of Children." International Journal of Environmental Research and Public Health 19, no. 1 (December 29, 2021): 324. http://dx.doi.org/10.3390/ijerph19010324.

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Head-related transfer functions (HRTFs) play a significant role in modern acoustic experiment designs in the auralization of 3-dimensional virtual acoustic environments. This technique enables us to create close to real-life situations including room-acoustic effects, background noise and multiple sources in a controlled laboratory environment. While adult HRTF databases are widely available to the research community, datasets of children are not. To fill this gap, children aged 5–10 years old were recruited among 1st and 2nd year primary school children in Aachen, Germany. Their HRTFs were measured in the hemi-anechoic chamber with a 5-degree × 5-degree resolution. Special care was taken to reduce artifacts from motion during the measurements by means of fast measurement routines. To complement the HRTF measurements with the anthropometric data needed for individualization methods, a high-resolution 3D-scan of the head and upper torso of each participant was recorded. The HRTF measurement took around 3 min. The children’s head movement during that time was larger compared to adult participants in comparable experiments but was generally kept within 5 degrees of rotary and 1 cm of translatory motion. Adult participants only exhibit this range of motion in longer duration measurements. A comparison of the HRTF measurements to the KEMAR artificial head shows that it is not representative of an average child HRTF. Difference can be seen in both the spectrum and in the interaural time delay (ITD) with differences of 70 μs on average and a maximum difference of 138 μs. For both spectrum and ITD, the KEMAR more closely resembles the 95th percentile of range of children’s data. This warrants a closer look at using child specific HRTFs in the binaural presentation of virtual acoustic environments in the future.
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Wang, Lei, Xiangyang Zeng, and Xiyue Ma. "Advancement of Individualized Head-Related Transfer Functions (HRTFs) in Perceiving the Spatialization Cues: Case Study for an Integrated HRTF Individualization Method." Applied Sciences 9, no. 9 (May 7, 2019): 1867. http://dx.doi.org/10.3390/app9091867.

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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.
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Yao, Dingding, Jiale Zhao, Longbiao Cheng, Junfeng Li, Xiaodong Li, Xiaochao Guo, and Yonghong Yan. "An individualization approach for head-related transfer function in arbitrary directions based on deep learning." JASA Express Letters 2, no. 6 (June 2022): 064401. http://dx.doi.org/10.1121/10.0011575.

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This paper provides an individualization approach for head-related transfer function (HRTF) in arbitrary directions based on deep learning by utilizing dual-autoencoder architecture to establish the relationship between HRTF magnitude spectrum and arbitrarily given direction and anthropometric parameters. In this architecture, one variational autoencoder (VAE) is utilized to extract interpretable and exploitable features of full-space HRTF spectra, while another autoencoder (AE) is employed for feature embedding of corresponding directions and anthropometric parameters. A deep neural networks model is finally trained to establish the relationship between these representative features. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of spectral distortion.
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Jenny, Claudia, and Christoph Reuter. "Usability of Individualized Head-Related Transfer Functions in Virtual Reality: Empirical Study With Perceptual Attributes in Sagittal Plane Sound Localization." JMIR Serious Games 8, no. 3 (September 8, 2020): e17576. http://dx.doi.org/10.2196/17576.

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Background In 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. Objective In 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. Methods For 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. Results With 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). Conclusions Both 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.
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Hur, Yoo-Mi, Young-Cheol Park, Seok-Pil Lee, and Dae-Hee Youn. "Efficient Individualization Method of HRTFs Using Critical-band Based Spectral Cue Control." Journal of the Acoustical Society of Korea 30, no. 4 (May 31, 2011): 167–80. http://dx.doi.org/10.7776/ask.2011.30.4.167.

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Singh, O. S., and S. P. Singh. "Providers' view of improving compliance of hormone replacement therapy (HRT) in primary care through counseling and individualization of treatment." International Journal of Gynecology & Obstetrics 70 (2000): B50. http://dx.doi.org/10.1016/s0020-7292(00)86264-5.

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Hargrove, J. T., and J. Beckum. "Utility of Estradiol and Progesterone Suspended in Propylene Glycol and Administered Transdermal by the Drop for More Accurate Individualization of HRT." Menopause 6, no. 4 (1999): 364–65. http://dx.doi.org/10.1097/00042192-199906040-00137.

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Gutierrez-Parera, Pablo, Jose J. Lopez, Javier M. Mora-Merchan, and Diego F. Larios. "Interaural time difference individualization in HRTF by scaling through anthropometric parameters." EURASIP Journal on Audio, Speech, and Music Processing 2022, no. 1 (May 12, 2022). http://dx.doi.org/10.1186/s13636-022-00241-y.

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AbstractHead-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.
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"Individualization of OC and HRT (O-247-248)." Human Reproduction 17, suppl 1 (July 1, 2002): 85–86. http://dx.doi.org/10.1093/humrep/17.suppl_1.85-a.

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Dissertations / Theses on the topic "HRTF individualization"

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Rugeles, Ospina Felipe. "Individualisation de l'écoute binaurale : création et transformation des indices spectraux et des morphologies des individus." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066209/document.

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Cette thèse se concentre sur l'individualisation des HRTF pour la synthèse binaurale. Les systèmes permettant de mesurer des HRTF sont difficiles d'accès au grand public, ce qui pose un problème pour la démocratisation de l'audio binaurale de haute qualité. C'est pour cette raison que nous recherchons une méthode permettant de calculer les HRTF d'une personne qu'à partir de la connaissance de sa morphologie. Ces travaux de thèse avancent sur les investigations visant à avoir une technique de personnalisation de l'audio binaurale. La technique de personnalisation proposée part du principe qu'il existe un lien direct et systématique liant des paramètres de la morphologie d'un individu avec les indices spectraux de ses HRTF. Elle est basée sur la détermination d'une fonction qui prend en entrée des paramètres morphologiques et qui donne en sortie des paramètres de transformation à appliquer à un jeu de HRTF existant pour obtenir un jeu de HRTF personnalisé. Cette fonction est estimée à partir d'analyses statistiques faits sur une base de données contenant des modèles numériques des individus ainsi que leurs HRTF. Pour atteindre cet objectif, il faut construire dans un premier temps les données à partir desquelles nous allons estimer notre fonction de personnalisation. Ces travaux expliquent les systèmes que nous avons conçus pour mesurer les HRTF des individus ainsi que pour obtenir leur modèle numérique tridimensionnel. De plus, les protocoles de mesure associés à chaque système sont expliqués. Enfin, nous expliquons comment les données obtenues peuvent être exploitées pour développer un procédé de personnalisation des HRTF
This thesis focuses on the HRTF individualization problem in the context of binaural synthesis for general applications. HRTF strongly depend on morphological features of a person and, in order to provide compelling auditory spaces, binaural synthesis requires the use of individualized HRTF. Measuring or calculating the HRTF of a listener are common but lengthy and costly methods that are not feasible for general public applications. That is the reason why we aim to develop an alternative technique to obtain customized HRTF. The technique proposed relies on estimating the spectral cues of the HRTF, corresponding to the colorations induced by pinna filtering, from a person's morphology. The spectral cues represent the most complex and individual part of HRTF. The work presented in this thesis is based on the existence of a direct and systematic link between the morphology of a person and the spectral cues of their HRTF. The goal is to start from individualization techniques of HRTF and make studies for better understanding the relationship between the morphology of a person and their HRTF. The ultimate goal is then to find a proper morphological matching personalization technique. The first step to achieve this goal is to build the measuring systems who will help us create the databases that we will use for our analyses. This thesis explains the two measuring systems that were created and the measuring protocols that were used to create two related databases containing the 3D models and the measured HRTFs of a collection of people. We then explain how these databases can be used to fit the HRTF individualization technique we have proposed
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Zhang, Mengqiu. "Experimental guided spherical harmonics based head-related transfer function modeling." Phd thesis, 2012. http://hdl.handle.net/1885/9796.

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In this thesis we investigate the experimental guided spherical harmonics based Head-Related Transfer Function (HRTF) modeling where HRTFs are parameterized as frequency and source location. We focus on efficiently representing the HRTF variations in sufficient detail by mathematical modeling and the experimental measurements. The goal of this work is towards an optimal functional HRTF modeling taking into account the demands of decreasing the computational cost and alleviating the HRTF interpolation and/or extrapolation in the headphone based binaural systems. To represent HRTF by models, we firstly consider the high variability of HRTFs among individuals caused by the differentiation of the scattering effects of the individual bodies on the sound waves. We conduct a series of statistical analyses on an experimental HRTF database of human subjects to reveal the correlation between the physical features of human beings, especially pinna, head, and torso, and the corresponding HRTFs. The strategy enables us to identify a minimal set of physical features which strongly influence the HRTFs in a direct physical way. We next consider the continuity of the HRTF representation in both spatial and frequency domain. We define a functional HRTF model class in which the HRTF spatial representation has been justified to be well approximated by a finite number of spherical harmonics while HRTF frequency representation remains the focus of this thesis. In order to seek an efficient representation for HRTF frequency portion, we derive a metric that is able to numerically evaluate the efficiency of different complete orthonormal bases. We show that the complex exponentials form the most efficient basis. Given the identified basis, we then provide a solution to determine the dimensionality of the representation. To represent HRTF by measurements, we firstly consider the required angular resolution and the most suitable sampling scheme taking into account the two dimensional angular direction and the wide audio frequency range. We review the spherical harmonic analysis of the HRTF from which the least required number of spatial samples for HRTF measurement is derived. Considering how the HRTF data should be sampled on the sphere, we propose a list of requirements for the determination of the HRTF measurement grid. In addition to explaining how to measure the HRTF over sphere according to the identified scheme, we propose a fast spherical harmonic transform algorithm. We next consider the feasible experimental setup for a non-anechoic situation, that is, the measurements can be made when there is some reverberation. We emphasize on the design of the test signal and the post-processing to extract HRTFs.
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Conference papers on the topic "HRTF individualization"

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Miccini, Riccardo, and Simone Spagnol. "HRTF Individualization using Deep Learning." In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2020. http://dx.doi.org/10.1109/vrw50115.2020.00084.

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Zhang, M., R. A. Kennedy, T. D. Abhayapala, and W. Zhang. "Statistical method to identify key anthropometric parameters in hrtf individualization." In 2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA 2011). IEEE, 2011. http://dx.doi.org/10.1109/hscma.2011.5942401.

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Fantini, Davide, Federico Avanzini, Stavros Ntalampiras, and Giorgio Presti. "HRTF Individualization Based on Anthropometric Measurements Extracted from 3D Head Meshes." In 2021 Immersive and 3D Audio: from Architecture to Automotive (I3DA). IEEE, 2021. http://dx.doi.org/10.1109/i3da48870.2021.9610904.

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He, Jianjun, Woon-Seng Gan, and Ee-Leng Tan. "On the preprocessing and postprocessing of HRTF individualization based on sparse representation of anthropometric features." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178047.

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