Sonify Anything: Towards Context-Aware Sonic Interactions in AR

Fuente: arXiv
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Hauptverfasser: Schütz, Laura, Matinfar, Sasan, Eck, Ulrich, Roth, Daniel, Navab, Nassir
Format: Preprint
Veröffentlicht: 2025
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author Schütz, Laura
Matinfar, Sasan
Eck, Ulrich
Roth, Daniel
Navab, Nassir
author_facet Schütz, Laura
Matinfar, Sasan
Eck, Ulrich
Roth, Daniel
Navab, Nassir
contents In Augmented Reality (AR), virtual objects interact with real objects. However, the lack of physicality of virtual objects leads to the absence of natural sonic interactions. When virtual and real objects collide, either no sound or a generic sound is played. Both lead to an incongruent multisensory experience, reducing interaction and object realism. Unlike in Virtual Reality (VR) and games, where predefined scenes and interactions allow for the playback of pre-recorded sound samples, AR requires real-time sound synthesis that dynamically adapts to novel contexts and objects to provide audiovisual congruence during interaction. To enhance real-virtual object interactions in AR, we propose a framework for context-aware sounds using methods from computer vision to recognize and segment the materials of real objects. The material's physical properties and the impact dynamics of the interaction are used to generate material-based sounds in real-time using physical modelling synthesis. In a user study with 24 participants, we compared our congruent material-based sounds to a generic sound effect, mirroring the current standard of non-context-aware sounds in AR applications. The results showed that material-based sounds led to significantly more realistic sonic interactions. Material-based sounds also enabled participants to distinguish visually similar materials with significantly greater accuracy and confidence. These findings show that context-aware, material-based sonic interactions in AR foster a stronger sense of realism and enhance our perception of real-world surroundings.
format Preprint
id arxiv_https___arxiv_org_abs_2508_01789
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Sonify Anything: Towards Context-Aware Sonic Interactions in AR
Schütz, Laura
Matinfar, Sasan
Eck, Ulrich
Roth, Daniel
Navab, Nassir
Human-Computer Interaction
Computer Vision and Pattern Recognition
Sound
Audio and Speech Processing
H.5.5; H.5.2; H.5.1; I.3.5
In Augmented Reality (AR), virtual objects interact with real objects. However, the lack of physicality of virtual objects leads to the absence of natural sonic interactions. When virtual and real objects collide, either no sound or a generic sound is played. Both lead to an incongruent multisensory experience, reducing interaction and object realism. Unlike in Virtual Reality (VR) and games, where predefined scenes and interactions allow for the playback of pre-recorded sound samples, AR requires real-time sound synthesis that dynamically adapts to novel contexts and objects to provide audiovisual congruence during interaction. To enhance real-virtual object interactions in AR, we propose a framework for context-aware sounds using methods from computer vision to recognize and segment the materials of real objects. The material's physical properties and the impact dynamics of the interaction are used to generate material-based sounds in real-time using physical modelling synthesis. In a user study with 24 participants, we compared our congruent material-based sounds to a generic sound effect, mirroring the current standard of non-context-aware sounds in AR applications. The results showed that material-based sounds led to significantly more realistic sonic interactions. Material-based sounds also enabled participants to distinguish visually similar materials with significantly greater accuracy and confidence. These findings show that context-aware, material-based sonic interactions in AR foster a stronger sense of realism and enhance our perception of real-world surroundings.
title Sonify Anything: Towards Context-Aware Sonic Interactions in AR
topic Human-Computer Interaction
Computer Vision and Pattern Recognition
Sound
Audio and Speech Processing
H.5.5; H.5.2; H.5.1; I.3.5
url https://arxiv.org/abs/2508.01789