Schedule as of Oct 11, 2022 - subject to change

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Wednesday, October 26 • 4:00pm - 4:15pm
Predicting Personalized Head Related Transfer Functions using Acoustic Scattering Neural Networks

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recent past. Binaural audio is one of the most convenient formats to deliver accurate spatial audio over headphones. Personalized Head-related Transfer Functions (HRTFs) are an integral component of binaural audio that determines the quality of the spatial audio experience. In this paper, we describe a novel technique to predict personalized HRTFs based on 2D images or a video capture. The state-of-the-art 3D reconstruction techniques were developed for generic objects and thus do not perform well with complex structures such as an ear. We propose a novel 3D reconstruction algorithm that is modeled taking into account the geometry of the ear. The 3D output is then fed to a Acoustic Scattering Neural Network (ASNN) designed on the principles of Boundary Element Method (BEM) that outputs personalized HRTFs. The personalized HRTFs predicted are then compared both objectively with the measured HRTFs. We discuss the results, limitations, and the caveats necessary for an accurate modeling of personalized HRTFs.

avatar for Nikhil Javeri

Nikhil Javeri

Manager - Machine Learning R&D, EmbodyVR Inc
As a Manager for Machine Learning R&D at Embody, I am tasked with overseeing projects that combine psychoacoustics with machine learning. I am currently working on projects that help characterize acoustic scattering around structural human topology using multi-modal machine learning... Read More →

Wednesday October 26, 2022 4:00pm - 4:15pm EDT
Online Papers
  Spatial Audio
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