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Deep Learning as a predictor for personalized head related transfer functions in virtual environments. Abstract One of the most impressive capabilities of the human auditory system is Author: Hens Zimmerman. A spherical basis function neural network for pole-zero modeling of head-related transfer functions. In: IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 92–Cited by: 2. A head-related transfer function personalized algorithm based on Locally Linear Embedding is proposed for the precise localization of human beings with different physiological parameters. HRTF data was processed to reduce dimensionality by Locally Linear Embedding at first and linearly fitted in the low-dimensional space to extract the Cited by: 1. To obtain personally the hearing feeling on the virtual sound field in an interactive computer environment, this paper presents a HRTF and neural network based prediction and simulation method for indoor sports acoustic. The method obtains room impulse response of random position through frequency domain interpolation, realizes motion predicting mechanism utilizing the .
Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the Brand: Elsevier Science. Soc. Am., Vol. 97, No. 1, , pp  R. Jenison, "A Spherical Basis Function Neural Network for Pole-Zero Modeling of Head-Related Transfer Functions", in Proc. of IEEE Workshop in Applications of Signal Processing to Audio and Acoustics, New York,  P. R. an improved deep neural network for modeling speaker characteristics at different temporal scales: auditory model based subsetting of head-related transfer function datasets: efficient representation and sparse sampling of head-related transfer functions using phase-correction based on ear alignment. The auditory nerve fibers project to the cochlear nuclear complex made up of the posteroventral (PVCN) and dorsal (DCN) cochlear nuclei and the anteroventral cochlear nucleus (AVCN) (refer to Fig. 4).A complete tonotopic organization is maintained in each subdivision. There is a varied taxonomy of cell types in the cochlear nuclei. The branch of the auditory nerve fiber that .
Jenison, Rick L. "A spherical basis function neural network Acoustical Society of America, 99(5): May for approximating acoustic scatter." Journal of the  Jenison, Rick L. "A spherical basis function neural network for pole-zero modeling of head-related transfer functions.". CVPR and NIPS Papers. Z. Zhang, Y. Xiang, L. Wu, B. Xue, and A. Nehorai, "KerGM: Kernelized graph matching," Advances in Neural Information Processing Systems. A spherical basis function neural network for pole-zero modeling of head-related transfer functions. Proceedings of Workshop on Applications of Signal Processing to Audio and Accoustics, Cardiac Deformation Recovery using a 3D Incompressible Deformable by: This book fills this need by systematically introducing mathematical and computational tools in precisely the contexts that first established their importance for neuroscience. All mathematical concepts will be introduced from the simple to complex using the most widely used computing environment, : Elsevier Science.