Sparse Head-Related Impulse Response for Efficient Direct Convolution

TL;DR

This paper introduces a novel sparse head-related impulse response (HRIR) factorization that enables efficient time-domain convolution for spatial audio synthesis, reducing computational costs while maintaining audio quality.

Contribution

It presents a new structural factorization of HRIRs into resonance and reflection filters, with the reflection filter made sparse, based on an extended non-negative matrix factorization algorithm.

Findings

Sparse reflection filters can be used with minimal distortion.

Time-domain convolution is more efficient than frequency-domain methods.

The method applies to arbitrary source signals.

Abstract

Head-related impulse responses (HRIRs) are subject-dependent and direction-dependent filters used in spatial audio synthesis. They describe the scattering response of the head, torso, and pinnae of the subject. We propose a structural factorization of the HRIRs into a product of non-negative and Toeplitz matrices; the factorization is based on a novel extension of a non-negative matrix factorization algorithm. As a result, the HRIR becomes expressible as a convolution between a direction-independent \emph{resonance} filter and a direction-dependent \emph{reflection} filter. Further, the reflection filter can be made \emph{sparse} with minimal HRIR distortion. The described factorization is shown to be applicable to the arbitrary source signal case and allows one to employ time-domain convolution at a computational cost lower than using convolution in the frequency domain.