Demo Data from EDBT 2026 / reviewers in the wild / expert
Mahitha Rachumalla
dblp:416/3780
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0001-1286-1241ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Immersive interaction · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › acoustic simulation
room impulse response synthesis |
0.9 | 1 | 2025 | Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic Rendering · UIST 2025 |
Audio and music processing › spatial audio
sound field reproduction |
0.9 | 1 | 2025 | Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic Rendering · UIST 2025 |
Methods — techniques the papers use, named apart from their topics
multimodal scene representation · 1.7acoustic calibration · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic RenderingabstractIn Extended Reality (XR), rendering sound that accurately simulates real-world acoustics is pivotal in creating lifelike and believable virtual experiences. However, existing XR spatial audio rendering methods often struggle with real-time adaptation to diverse physical scenes, causing a sensory mismatch between visual and auditory cues that disrupts user immersion. To address this, we introduce SAMOSA, a novel on-device system that renders spatially accurate sound by dynamically adapting to its physical environment. SAMOSA leverages a synergistic multimodal scene representation by fusing real-time estimations of room geometry, surface materials, and semantic-driven acoustic context. This rich representation then enables efficient acoustic calibration via scene priors, allowing the system to synthesize a highly realistic Room Impulse Response (RIR). We validate our system through technical evaluation using acoustic metrics for RIR synthesis across various room configurations and sound types, alongside an expert evaluation (N=12). Evaluation results demonstrate SAMOSA's feasibility and efficacy in enhancing XR auditory realism. Tianyu Xu 0008, Jihan Li, Penghe Zu, Pranav Sahay, Maruchi Kim, Jack Obeng-Marnu, Farley Miller, Xun Qian, Katrina Passarella, Mahitha Rachumalla, Rajeev Nongpiur, D. Shin |
UIST | 10 |