April 26, 2024, 4:44 a.m. | Arjun Somayazulu, Sagnik Majumder, Changan Chen, Kristen Grauman

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.16216v1 Announce Type: new
Abstract: An environment acoustic model represents how sound is transformed by the physical characteristics of an indoor environment, for any given source/receiver location. Traditional methods for constructing acoustic models involve expensive and time-consuming collection of large quantities of acoustic data at dense spatial locations in the space, or rely on privileged knowledge of scene geometry to intelligently select acoustic data sampling locations. We propose active acoustic sampling, a new task for efficiently building an environment acoustic …

abstract arxiv audio collection cs.cv cs.ro cs.sd data eess.as environment exploration location locations modeling sound space spatial type visual

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Consultant - Artificial Intelligence & Data (Google Cloud Data Engineer) - MY / TH

@ Deloitte | Kuala Lumpur, MY