Binaural Signal Matching with Wearable Arrays for Near-Field Sources

TL;DR

This paper evaluates and enhances the Binaural Signal Matching algorithm for near-field sound sources, demonstrating that near-field modeling significantly improves binaural reproduction accuracy for close-range sources.

Contribution

It introduces a near-field BSM approach that accounts for source distance, improving reproduction quality for near-field scenarios compared to traditional far-field methods.

Findings

Far-field BSM performs well up to tens of centimeters from the source.

Near-field BSM reduces binaural error for very-close sources.

Near-field modeling enhances immersive audio experiences in VR and teleconferencing.

Abstract

Binaural reproduction methods aim to recreate an acoustic scene for a listener over headphones, offering immersive experiences in applications such as Virtual Reality (VR) and teleconferencing. Among the existing approaches, the Binaural Signal Matching (BSM) algorithm has demonstrated high quality reproduction due to its signal-independent formulation and the flexibility of unconstrained array geometry. However, this method assumes far-field sources and has not yet been investigated for near-field scenarios. This study evaluates the performance of BSM for near-field sources. Analysis of a semi-circular array around a rigid sphere, modeling head-mounted devices, show that far-field BSM performs adequately for sources up to approximately tens of centimeters from the array. However, for sources closer than this range, the binaural error increases significantly. Incorporating a near-field BSM design, which accounts for the source distance, significantly reduces the error, particularly for these very-close distances, highlighting the benefits of near-field modeling in improving reproduction accuracy.