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Relevant bibliographies by topics / HRTF modeling

Academic literature on the topic 'HRTF modeling'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "HRTF modeling"

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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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Huang, Wanqiu, Xiangyang Zeng, and lei Wang. "Personalization Method for HRTF Based on Multi-Dimensional Physiological Parameters." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 281–86. http://dx.doi.org/10.1051/jnwpu/20183620281.

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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.

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Zhao, Manlin, Zhichao Sheng, and Yong Fang. "Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements." Applied Sciences 12, no. 16 (August 15, 2022): 8155. http://dx.doi.org/10.3390/app12168155.

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In this paper, we propose a global personalized head-related transfer function (HRTF) method based on anthropometric measurements and ear images. The model consists of two sub-networks. The first is the VGG-Ear Model, which extracts features from the ear images. The second sub-network uses anthropometric measurements, ear features, and frequency information to predict the spherical harmonic (SH) coefficients. Finally, the personalized HRTF is obtained through inverse spherical harmonic transform (SHT) reconstruction. With only one training, the HRTF in all directions can be obtained, which greatly reduces the parameters and training cost of the model. To objectively evaluate the proposed method, we calculate the spectral distance (SD) between the predicted HRTF and the actual HRTF. The results show that the SD provided by this method is 5.31 dB, which is better than the average HRTF of 7.61 dB. In particular, the SD value is only increased by 0.09 dB compared to directly using the pinna measurements.

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Lee, Geon, and Hong Kim. "Personalized HRTF Modeling Based on Deep Neural Network Using Anthropometric Measurements and Images of the Ear." Applied Sciences 8, no. 11 (November 7, 2018): 2180. http://dx.doi.org/10.3390/app8112180.

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This paper proposes a personalized head-related transfer function (HRTF) estimation method based on deep neural networks by using anthropometric measurements and ear images. The proposed method consists of three sub-networks for representing personalized features and estimating the HRTF. As input features for neural networks, the anthropometric measurements regarding the head and torso are used for a feedforward deep neural network (DNN), and the ear images are used for a convolutional neural network (CNN). After that, the outputs of these two sub-networks are merged into another DNN for estimation of the personalized HRTF. To evaluate the performance of the proposed method, objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated one are measured. Consequently, the proposed method provides the RMSE of −18.40 dB and LSD of 4.47 dB, which are lower by 0.02 dB and higher by 0.85 dB than the DNN-based method using anthropometric data without pinna measurements, respectively. Next, a sound localization test is performed for the subjective evaluation. As a result, it is shown that the proposed method can localize sound sources with higher accuracy of around 11% and 6% than the average HRTF method and DNN-based method, respectively. In addition, the reductions of the front/back confusion rate by 12.5% and 2.5% are achieved by the proposed method, compared to the average HRTF method and DNN-based method, respectively.

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Guldenschuh, Markus, Alois Sontacchi, Franz Zotter, and Robert Höldrich. "HRTF modeling in due consideration variable torso reflections." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3080. http://dx.doi.org/10.1121/1.2932888.

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Bai, Mingsian R., and Teng-Chieh Tsao. "Numerical Modeling of Head-Related Transfer Functions Using the Boundary Source Representation." Journal of Vibration and Acoustics 128, no. 5 (April 4, 2006): 594–603. http://dx.doi.org/10.1115/1.2203337.

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A technique based on the virtual source representation is presented for modeling head-related transfer functions (HRTFs). This method is motivated by the theory of simple layer potential and the principle of wave superposition. Using the virtual source representation, the HRTFs for a human head with pinnae are calculated with a minimal amount of computation. In the process, a special regularization scheme is required to calculate the equivalent strengths of virtual sources. To justify the proposed method, tests were carried out to compare the virtual source method with the boundary element method (BEM) and a direct HRTF measurement. The HRTFs obtained using the virtual source method agrees reasonably well in terms of frequency response, directional response, and impulse response with the other methods. From the numerical perspectives, the virtual source method obviates the singularity problem as commonly encountered in the BEM, and is less computationally demanding than the BEM in terms of computational time and memory storage. Subjective experiments are also conducted using the calculated and the measured HRTFs. The results reveal that the spatial characteristics of sound localization are satisfactorily reproduced as a human listener would naturally perceive by using the virtual source HRTFs.

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Dat, Tran H., Itakura Fumitada, and Igor L. Oboznenko. "Modeling HRTF of a thin elastic layered spherical model of head." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2388. http://dx.doi.org/10.1121/1.4777842.

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Zhang, Mengqiu, Rodney A. Kennedy, and Thushara D. Abhayapala. "Empirical Determination of Frequency Representation in Spherical Harmonics-Based HRTF Functional Modeling." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 2 (February 2015): 351–60. http://dx.doi.org/10.1109/taslp.2014.2381881.

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Ramos, Oscar Alberto, and Fabián Carlos Tommasini. "Magnitude Modelling of HRTF Using Principal Component Analysis Applied to Complex Values." Archives of Acoustics 39, no. 4 (March 1, 2015): 477–82. http://dx.doi.org/10.2478/aoa-2014-0051.

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Abstract Principal components analysis (PCA) is frequently used for modelling the magnitude of the head-related transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between -45°. and +9°.

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Terai, Kenichi, and Isao Kakuhari. "HRTF calculation with less influence from 3-D modeling error: Making a physical human head model from geometric 3-D data." Acoustical Science and Technology 24, no. 5 (2003): 333–34. http://dx.doi.org/10.1250/ast.24.333.

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Dissertations / Theses on the topic "HRTF modeling"

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Gangam, Srikanth. "Optical Investigations of Cd Free Cu₂ZnSnS₄ Solar Cells." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1345088305.

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Gajdoš, Martin. "Porovnání metod efektivní a funkční konektivity ve funkční magnetické rezonanci." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219732.

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Functional magnetic resonance imaging (fMRI) is recent important method, used in neuroimaging. The aim of this thesis is to develop software tool for comparison of two methods for functional and effective connectivity estimation. In this thesis are described the basics of magnetic resonance imaging, fMRI, basic terms of fMRI experiments and generally are described methods of functional and effective connectivity. Then are more detailed mentioned methods of dynamic causal modeling (DCM), Granger causal modeling (GCM) and independent component analysis (ICA). Practical implementation of DCM in toolbox SMP and ICA in toolbox GIFT is also mentioned. In purpose to describe behavior of DCM and GCM in dependence on several parameters are performed Monte Carlo simulations. Then the concept and realization of software tool for simulating connectivity and comparison of DCM and GCM are described. Finally results of DCM and GCM comparison and results of Monte Carlo simulations are discussed.

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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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Ou, De-Jian, and 歐德健. "Designs of Low-Order Modeling HRTF and Crosstalk Cancellation Using Wavelet Sub-band Recursive Least-Square Adaptive Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90584038691392296320.

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碩士
國立成功大學
電機工程學系碩博士班
96
Head-Related Transfer Function is the channel function which is measured by the experiment. However, the size of the data is so large that we apply them to synthesize 3-D audio arduously. We will reduce the HRTF and retain the important date to manipulate conveniently. In order to realize 3-D audio in the environment of two loudspeakers, it is a must consideration that the condition of crosstalk. On account of crosstalk, we need to design crosstalk canceller to improve the condition. We use the different adaptive algorithm to design this filter, and propose wavelet sub-band recursive least-square adaptive algorithm to improve the computational complexity of recursive least-square algorithm.

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Liu, C. J., and 劉致榮. "Common-Acoustic-Poles/Zeros Modeling and Clustering of HRTFs." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/60007816490820772897.

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碩士
國立交通大學
電信工程系
91
Head-related transfer functions (HRTFs) used in 3-D sound processing are locationally dependent. Fully modeling sound localization cues around the head burdens implementation with a huge data set. Low-order IIR filters are an efficient way to model HRTFs. To further reduce the required memory for storage of model parameters and save computational cost, we propose jointly balanced model truncation for design of common-pole IIR filters. In common-acoustic-poles/zeros (CAPZ) modeling, poles are independent of sound directions and zeros are used to show differences between HRTFs. Not all measured HRTFs have common-pole characteristics, so it is impractical to force all HRTFs share common poles. It is more practical to divide HRTFs into groups and each group has its own common poles. Therefore, clustering of HRTFs becomes essential. We propose an algorithm, common-pole LBG, which iteratively updates the grouping to reach the optimal clustering. Finally, computer simulations are used to verify our proposed methods. We will demonstrate our proposed methods have advantage over previous works.

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Zhang, Wen. "Measurement and modelling of head-related transfer function for spatial audio synthesis." Phd thesis, 2010. http://hdl.handle.net/1885/9825.

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There has been a growing interest in spatial sound generation arising from the development of new communications and media technologies. Binaural spatial sound systems are capable of encoding and rendering sound sources accurately in three dimensional space using only two recording/playback channels. This is based on the concept of the Head-Related Transfer Function (HRTF), which is a set of acoustic filters from the sound source to a listener's eardrums and contains all the listening cues used by the hearing mechanism for decoding spatial information encoded in binaural signals. The HRTF is usually obtained from acoustic measurements on different persons. In the case of discrete data and sets of measurements corresponding to different human subjects, it is desirable to have a continuous functional representation of the HRTF for efficiently rendering moving sounds in the virtual spatial audio systems; further this representation should be well-suited for customization to an individual listener. In this thesis, modal analysis is applied to examine the HRTF data structure, that is to employ the wave equation solutions to expand the HRTF with separable basis functions. This leads to a general representation of the HRTF into separated spatial and spectral components, where the spatial basis functions modes account for the HRTF spatial variations and the remaining HRTF spectral components provide a new means to examine the human body scattering behavior. The general model is further developed into the HRTF continuous functional representations. We use the normalized spatial modes to link near-field and far-field HRTFs directly, which provides a way to obtain the HRTFs at different ranges from measurements conducted at only a single range. The spatially invariant HRTF spectral components are represented continuously using an orthogonal series. Both spatial and spectral basis functions are well known functions, thus the developed analytical model can be used to easily examine the HRTF data feature-individualization. An important finding of this thesis is that the HRTF decomposition with the spatial basis functions can be well approximated by a finite number, which is defined as the HRTF spatial dimensionality. The dimensionality determines the least number of the HRTF measurements in space. We perform high resolution HRTF measurements on a KEMAR mannequin in a semi-anechoic acoustic chamber. Both signal processing aspects to extract HRTFs from the raw measurements and a practical high resolution spatial sampling scheme have been given in this thesis.

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Yi-YangChuang and 莊翼陽. "Comparison of Analysis on Modeling the fMRI Data with Flexible HRF." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99931198855397295760.

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碩士
國立成功大學
統計學系碩博士班
100
In functional magnetic resonance imaging (fMRI) data analysis, haemodynamic response function (HRF) describes the temporal dynamic of the brain region response during activations. Canonical HRF is described as the ideal, noiseless response to an infinitesimally brief stimulus. Most conventional statistical methods employ the canonical HRF which is convolved with the stimuli function as the regressor called expected BOLD response in design matrix. In fact, the usage of expected BOLD response encounters a severe interference of uncertain noise, especially in fast event-related fMRI experiment. We expect that certain voxels will not activate at some time point because of losing concentration or other unknown reasons. In order to increase the flexibility in temporal dynamic of the HRF, we consider two methods to estimate the effect of stimuli. First, we use LASSO which is combined with a general linear model (GLM). Second, we use the Bayesian semi- and non-parametric modeling on our data and build a mixed effects model with a Dirichlet process prior for the distribution of the random effects. The selected activated voxels will be compared by these two methods. We can acquire more accurate and reasonable activated region than the previous methods on these fMRI data set through the usage of the flexible HRF. LASSO help us selecting the clustered area where the voxels have the real response to the stimulus. Non-parametric model can identify the similar pattern in the voxels and these voxels are classified into the same cluster. It can also be applied to find the period of time points where the BOLD signals had a severe impact by unknown nuisance.

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Book chapters on the topic "HRTF modeling"

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Chen, Wei, Ruimin Hu, Xiaochen Wang, and Dengshi Li. "HRTF Representation with Convolutional Auto-encoder." In MultiMedia Modeling, 605–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_49.

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Picinali, Lorenzo, and Brian F. G. Katz. "System-to-User and User-to-System Adaptations in Binaural Audio." In Sonic Interactions in Virtual Environments, 115–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04021-4_4.

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AbstractThis chapter concerns concepts of adaption in a binaural audio context (i.e. headphone-based three-dimensional audio rendering and associated spatial hearing aspects), considering first the adaptation of the rendering system to the acoustic and perceptual properties of the user, and second the adaptation of the user to the rendering quality of the system. We start with an overview of the basic mechanisms of human sound source localisation, introducing expressions such as localisation cues and interaural differences, and the concept of the Head-Related Transfer Function (HRTF), which is the basis of most 3D spatialisation systems in VR. The chapter then moves to more complex concepts and processes, such as HRTF selection (system-to-user adaptation) and HRTF accommodation (user-to-system adaptation). State-of-the-art HRTF modelling and selection methods are presented, looking at various approaches and at how these have been evaluated. Similarly, the process of HRTF accommodation is detailed, with a case study employed as an example. Finally, the potential of these two approaches are discussed, considering their combined use in a practical context, as well as introducing a few open challenges for future research.

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Morioka, Shuhei, Isao Nambu, Shohei Yano, Haruhide Hokari, and Yasuhiro Wada. "Adaptive Modeling of HRTFs Based on Reinforcement Learning." In Neural Information Processing, 423–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34478-7_52.

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Conference papers on the topic "HRTF modeling"

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Gebru, Israel D., Dejan Markovic, Alexander Richard, Steven Krenn, Gladstone A. Butler, Fernando De la Torre, and Yaser Sheikh. "Implicit HRTF Modeling Using Temporal Convolutional Networks." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414750.

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Zhang, Mengfan, Jui-Hsien Wang, and Doug L. James. "Personalized HRTF Modeling Using DNN-Augmented BEM." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414448.

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Li, Lin, and Qinghua Huang. "HRTF personalization modeling based on RBF neural network." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638350.

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Zhang, Mengqiu, Rodney A. Kennedy, and Thushara D. Abhayapala. "Efficiency evaluation and orthogonal basis determination in functional HRTF modeling." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946326.

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Bonacina, L., A. Canclini, F. Antonacci, M. Marcon, A. Sarti, and S. Tubaro. "A low-cost solution to 3D pinna modeling for HRTF prediction." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471685.

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Romigh, Griffin D., Douglas Brungart, Richard M. Stern, and Brian D. Simpson. "The role of spatial detail in sound-source localization: Impact on HRTF modeling and personalization." In ICA 2013 Montreal. ASA, 2013. http://dx.doi.org/10.1121/1.4799575.

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Miccini, Riccardo, and Simone Spagnol. "A hybrid approach to structural modeling of individualized HRTFs." In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2021. http://dx.doi.org/10.1109/vrw52623.2021.00022.

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Zhang, Mengfan, Xihong Wu, and Tianshu Qu. "Individual Distance-Dependent HRTFS Modeling Through A Few Anthropometric Measurements." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9052928.

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Xi, Jingwei, Wen Zhang, and Thushara D. Abhayapala. "Magnitude Modelling of Individualized HRTFs Using DNN Based Spherical Harmonic Analysis." In 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2021. http://dx.doi.org/10.1109/waspaa52581.2021.9632704.

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Karami, M. Amin, and Daniel J. Inman. "Nonlinear Dynamics of the Hybrid Rotary-Translational Energy Harvester." In ASME 2013 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/smasis2013-3110.

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The analytical modeling and experimental investigation of a nonlinear electromagnetic rotational energy harvester, which can harvest power from rotary and translational excitations, are presented. Some application of energy harvesting such as energy harvesting for tire pressure sensing require an energy harvester which is efficient in generating power from rotational ambient vibrations. The majority of literature on vibration energy harvesting assumes that the ambient excitations are along a single axis. The vibrations from human motion or rotary machines have two components of translational motion as well as a strong rotary motion. The energy harvesting device studied in this paper is a pendulum like device. The base excitations result in rotations of a pendulum. The pendulum is connected to a direct current micro generator. The rotational vibrations of the pendulum generates electricity through the DC generator. Since the energy harvester is responsive to both translational and rotational base excitations, it is called Hybrid Rotary-Translational (HRT) generator. In this paper a small size HRT harvester is introduced and modeled. The model is used to investigate the relation between the frequency and the amplitude of base vibrations on the vibrations and power generation characteristics of the HRT system. For each frequency and amplitude of vibrations the coexistence of multiple solutions and their basin of attractions are investigated. Three types of ambient excitations are studied: rotational, translational along the direction of gravity, and translational normal to the direction of the gravity.

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