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Sonic Interactions in Virtual Environments: the Egocentric Audio Perspective of the Digital Twin

arXiv 2204.09919 ↗First posted 21 Apr 2022cs.HC cs.AI cs.MMcited by 0

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Abstract (arXiv)

The relationships between the listener, physical world and virtual environment (VE) should not only inspire the design of natural multimodal interfaces but should be discovered to make sense of the mediating action of VR technologies. This chapter aims to transform an archipelago of studies related to sonic interactions in virtual environments (SIVE) into a research field equipped with a first theoretical framework with an inclusive vision of the challenges to come: the egocentric perspective of the auditory digital twin. In a VE with immersive audio technologies implemented, the role of VR simulations must be enacted by a participatory exploration of sense-making in a network of human and non-human agents, called actors. The guardian of such locus of agency is the auditory digital twin that fosters intra-actions between humans and technology, dynamically and fluidly redefining all those configurations that are crucial for an immersive and coherent experience. The idea of entanglement theory is here mainly declined in an egocentric-spatial perspective related to emerging knowledge of the listener's perceptual capabilities. This is an actively transformative relation with the digital twin potentials to create movement, transparency, and provocative activities in VEs. The chapter contains an original theoretical perspective complemented by several bibliographical references and links to the other book chapters that have contributed significantly to the proposal presented here.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 144 references that are in the map. First posted on arXiv 21 Apr 2022.

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References · 2

  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  2. 2018Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networkscited by 85

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