The spatial computing industry has seen exponential growth and complexity, driven by the rise of interactive experiences. Traditional stereo audio formats are insufficient for these immersive environments, leading to the adoption of spatial audio formats such as Ambisonics, object-based, and multichannel formats like Dolby Atmos. These advanced audio formats provide a more immersive soundscape, enhancing the overall user experience.
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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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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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