Search arXivSearch

arXiv · 2502.02929

AudioMiXR: Spatial Audio Object Manipulation with 6DoF for Sound Design in Augmented Reality

Abstract

We present AudioMiXR, an augmented reality (AR) interface intended to assess how users manipulate virtual audio objects situated in their physical space using six degrees of freedom (6DoF) deployed on a head-mounted display (Apple Vision Pro) for 3D sound design. Existing tools for 3D sound design are typically constrained to desktop displays, which may limit spatial awareness of mixing within the execution environment. Utilizing an XR HMD to create soundscapes may provide a real-time test environment for 3D sound design, as modern HMDs can provide precise spatial localization assisted by cross-modal interactions. However, there is no research on design guidelines specific to sound design with 6DoF in XR. To provide a first step toward identifying design-related research directions in this space, we conducted an exploratory study where we recruited 27 participants, consisting of expert and non-expert sound designers. The goal was to assess design lessons that can be used to inform future research venues in 3D sound design. We ran a within-subjects study where users designed both a music and cinematic soundscapes. After thematically analyzing participant data, we constructed two design lessons: (1) Proprioception for AR Sound Design, and (2) Balancing Audio-Visual Modalities in AR GUIs. Additionally, we provide application domains that can benefit most from 6DoF sound design based on our results. To expand on these insights, we conducted a second within-subjects study comparing AudioMiXR to a 2D panner baseline. Results show that AudioMiXR significantly improved usability (SUS), reduced frustration and mental workload (NASA-TLX), and enhanced creativity across all subscales. These findings demonstrate that 6DoF AR interaction yields measurable gains in user experience and creative output, positioning AudioMiXR as a promising foundation for future AR-based sound design tools.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brandon Woodard, Margarita Geleta, Joseph J. LaViola Jr., Andrea Fanelli, Rhonda Wilson. 2025-08-05. AudioMiXR: Spatial Audio Object Manipulation with 6DoF for Sound Design in Augmented Reality. https://arxiv.org/abs/2502.02929

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC

VisCanvas: A Node-Based Interface for Exploratory Visualization Authoring with LLMs

Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models (LLMs), substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas facilitates more diverse data interaction while maintaining performance levels (i.e., cognitive load and usability) that are indistinguishable from current prevailing methods. We then distill design principles for future AI-assisted visualization authoring environments. All supplemental materials required to reproduce the study are available at https://osf.io/gsxhn.

cs.HC