Multi-Channel Low-Rank Convolution of Jointly Compressed Room Impulse Responses

Abstract

The room impulse response (RIR) describes the response of a room to an acoustic excitation signal and models the acoustic channel between a point source and receiver. RIRs are used in a wide range of applications, e.g., virtual reality. In such an application, the availability of closely spaced RIRs and the capability to achieve low latency are imperative to provide an immersive experience. However, representing a complete acoustic environment using a fine grid of RIRs is prohibitive from a storage point of view and without exploiting spatial proximity, acoustic rendering becomes computationally expensive. We therefore propose two methods for the joint compression of multiple RIRs, based on the generalized low-rank approximation of matrices (GLRAM), for the purpose of efficiently storing RIRs and allowing for low-latency convolution. We show how one of the components of the GLRAM decomposition is virtually invariant to the change of position of the source throughout the room and how this can be exploited in the modeling and convolution. In simulations we show how this offers high compression, with less quality degradation than comparable benchmark methods.

Keywords

• Convolution
• low-rank modeling
• room impulse responses