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  1. itsadive.create.aau.dk

    Figure 2: Data structure, called HRTF patch, used as input to the autoencoder by Yamamoto and Igarashi. Figure reproduced from [28]. Since there is not a clear consensus on what is the most effective strategy for deep learning based HRTF individualization, this paper investigates further methods — in particular using newly developed
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  3. ieeexplore.ieee.org

    The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques. HRTF individualization is paramount for accurate binaural rendering, which is used in XR technologies, tools for the visually impaired, and many other applications. The rising availability of public HRTF data currently allows experimentation with different ...
    Author:Riccardo Miccini, Simone SpagnolPublished:2020
  4. Dive into the research topics of 'HRTF individualization using deep learning'. Together they form a unique fingerprint. ... Miccini, R & Spagnol, S 2020, HRTF individualization using deep learning. in Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Workshops (VRW 2020)., 9090538, IEEE, ...
    Author:Riccardo Miccini, Simone SpagnolPublished:2020
  5. In particular, its three components are: A generic head-and-torso component, taken from the "pinna-less" KEMAR set included in the Viking HRTF dataset v2 [2] with ITD removed (measured component); A fully customized pinna component, built using features related to the shape of the user's pinnae through deep learning [1,3] (synthesized component);
  6. itsadive.create.aau.dk

    the use of deep learning (DL) models and, starting from a previous work from the authors [18], offers the following contributions: • a deep-learning-based solution for synthesizing pinna-related responses (PRTFs) from user pictures; • a hybrid approach for combining such PRTFs with the best-matching interaural time difference from a dataset ...
  7. pubs.aip.org

    Among the existing HRTF individualization methods, the most straightforward way is HRTF selection, in which the most relevant HRTFs from publicly available HRTF databases are chosen as the individual HRTFs according to anthropometric similarities (Zotkin et al., 2003), perceptual similarities (Roginska et al., 2010), or their combination (Pelzer et al., 2020; Shu-Nung et al., 2017).
  8. In particular, its three components are: A generic head-and-torso component, taken from the "pinna-less" KEMAR set included in the Viking HRTF dataset v2 [2] with ITD removed (measured component); A fully customized pinna component, built using features related to the shape of the user's pinnae through deep learning [1,3] (synthesized component);
  9. itsadive.create.aau.dk

    Last Sunday, we presented the paper "HRTF individualization using deep learning" (R. Miccini, S. Spagnol) at the IEEE 5th VR Workshop on Sonic Interactions in Virtual Environments, as part of the 27th IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR 2020).Due to the pandemic outbreak, the conference has been an entirely virtual event, and the workshop was live-streamed ...
  10. semanticscholar.org

    The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques, and the knowledge acquired throughout the development and troubleshooting phases highlights areas of improvement which are expected to pave the way to more accurate models for HRTF individualization. The research presented in this paper focuses on Head-Related ...

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