Room Geometry

Room geometry inference, focusing on estimating room dimensions, shape, and surface properties from acoustic signals, is a crucial area of research with applications in 3D audio rendering, digital twin creation, and environmental modeling. Current research employs diverse approaches, including machine learning models like convolutional neural networks and transformers, as well as algorithms based on analyzing room impulse responses (RIRs) and exploiting the geometric relationships between direct and reflected sound. These methods aim to overcome limitations of traditional techniques by handling complex geometries, noisy signals, and the absence of first-order reflections, improving accuracy and robustness. The resulting advancements have significant implications for various fields, enabling more realistic virtual environments, improved spatial audio technologies, and enhanced understanding of acoustic spaces.

Papers