Volumetric Amplitude Panning and Diffusion for Spatial Audio Production
This paper explores the limitations of traditional approaches to 3D panning such as VBAP and MDAP, and presents a new approach which resolves those limitations. This research represents a significant step forward in the creation of a more reliable virtual production workflow for spatial audio.
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Predicting Personalized Head Related Transfer Functions using Acoustic Scattering Neural Networks
Existing state-of-the-art 3D reconstruction techniques were developed for generic objects and do not perform well with complex structures such as the human ear. Here we detail our Machine Learning-based approach for estimating personalized HRTFs for production, gaming, cinematic, and VR/XR applications on a mass scale.
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