Compression of room impulse responses for compact storage and fast low-latency convolution

Martin Jälmby*, Filip Elvander, Toon van Waterschoot

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
19 Downloads (Pure)

Abstract

Room impulse responses (RIRs) are used in several applications, such as augmented reality and virtual reality. These applications require a large number of RIRs to be convolved with audio, under strict latency constraints. In this paper, we consider the compression of RIRs, in conjunction with fast time-domain convolution. We consider three different methods of RIR approximation for the purpose of RIR compression and compare them to state-of-the-art compression. The methods are evaluated using several standard objective quality measures, both channel-based and signal-based. We also propose a novel low-rank-based algorithm for fast time-domain convolution and show how the convolution can be carried out without the need to decompress the RIR. Numerical simulations are performed using RIRs of different lengths, recorded in three different rooms. It is shown that compression using low-rank approximation is a very compelling option to the state-of-the-art Opus compression, as it performs as well or better than on all but one considered measure, with the added benefit of being amenable to fast time-domain convolution.

Original languageEnglish
Article number45
Number of pages23
JournalEurasip Journal on Audio, Speech, and Music Processing
Volume2024
Issue number1
DOIs
Publication statusPublished - Dec 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Convolution
  • Low-rank modeling
  • Room impulse responses
  • Tensor decomposition

Fingerprint

Dive into the research topics of 'Compression of room impulse responses for compact storage and fast low-latency convolution'. Together they form a unique fingerprint.

Cite this