Optimized Loudspeaker Panning for Adaptive Sound-Field Correction and Non-stationary Listening Areas

TL;DR

This paper presents Bayesian normalization and content panning optimization techniques to improve sound-field correction in surround sound systems with variable layouts and listening areas, enhancing audio quality.

Contribution

It introduces novel Bayesian and optimization methods for adaptive loudspeaker normalization and panning, addressing non-standard layouts and non-stationary listening positions.

Findings

Bayesian adaptation improves robustness to layout variations.

Optimized panning enhances sound-field accuracy.

Methods are effective in practical, variable environments.

Abstract

Surround sound systems commonly distribute loudspeakers along standardized layouts for multichannel audio reproduction. However in less controlled environments, practical layouts vary in loudspeaker quantity, placement, and listening locations / areas. Deviations from standard layouts introduce sound-field errors that degrade acoustic timbre, imaging, and clarity of audio content reproduction. This work introduces both Bayesian loudspeaker normalization and content panning optimization methods for sound-field correction. Conjugate prior distributions over loudspeaker-listener directions update estimated layouts for non-stationary listening locations; digital filters adapt loudspeaker acoustic responses to a common reference target at the estimated listening area without acoustic measurements. Frequency-domain panning coefficients are then optimized via sensitivity / efficiency objectives subject to spatial, electrical, and acoustic domain constraints; normalized and panned loudspeakers form virtual loudspeakers in standardized layouts for accurate multichannel reproduction. Experiments investigate robustness of Bayesian adaptation, and panning optimizations in practical applications.