Joint Estimation of the Room Geometry and Modes with Compressed Sensing

Abstract

Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response. If we rely on the standard uniform sampling, the estimation of room impulse response for arbitrary positions in the room requires a large number of measurements. In order to lower the required sampling rate, some solutions have emerged that exploit the sparse representation of the room wavefield in the terms of plane waves in the lowfrequency domain. The plane wave representation has a simple form in rectangular rooms. In our solution, we observe the basic axial modes of the wave vector grid for extraction of the room geometry and then we propagate the knowledge to higher order modes out of the low-pass version of the measurements. Estimation of the approximate structure of the kspace should lead to the reduction in the terms of number of required measurements and in the increase of the speed of the reconstruction without great losses of quality.

Perceptual Matching of Room Acoustics for Auditory Augmented Reality in Small Rooms - Literature Review and Theoretical Framework

“…The impression of the room size is likely to play an essential role in the perceived agreement of virtual room acoustics with the real environment. There have been attempts to estimate the size, shape (Tukuljac et al, 2018; Kim et al, 2017) and volume (Shabtai et al, 2010; Genovese et al, 2019) of rooms blindly. However, the auditory perception of room size does not linearly depend on the room’s actual size.…”

Low-Rank Tensor Modeling of Room Impulse Responses

“…The idea of modeling an RIR as a sum of decaying sinusoids is well established in the literature. In works such as [16]- [18] this has been used for lower frequencies. The idea of lowrank modeling of RIRs is also anchored in previous work.…”