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Individualized HRTF-based Binaural Renderer for Higher-Order Ambisonics

Ambisonics is a promising spatial sound technique in augmented and virtual reality. In our previous study, we modeled the individual head-related transfer functions (HRTFs) using deep neural networks based on spatial principal component analysis. This paper proposes an individualized HRTF-based binaural renderer for the higher-order Ambisonics. The binaural renderer is implemented by filtering the virtual loudspeaker signals using individualized HRTFs. We perform subjective experiments to evaluate generic and individualized binaural renderers. Results show that the individualized binaural renderer has front-back confusion rates that are significantly lower than those of the generic binaural renderer. Therefore, we validate that using individualized HRTFs to convolve with those virtual loudspeaker signals to generate virtual sound at an arbitrary spatial direction still performs better than those using generic HRTFs. In addition, by measuring or modeling individual’s HRTFs in a small set of directions, our proposed binaural renderer system effectively predict individual’s HRTFs in arbitrary spatial directions.

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