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

2025-10-27

AI Summary

To address sound field distortions—including phantom source localization errors, timbral coloration, and reduced speech intelligibility—arising from non-standard loudspeaker layouts and dynamic listening regions, this paper proposes a Bayesian-driven adaptive immersive audio rendering method. The approach employs conjugate priors to model the relative angular geometry between loudspeakers and listeners, enabling loudspeaker normalization and virtual loudspeaker reconstruction without requiring measured acoustic impulse responses. Coupled with frequency-domain immersive audio coefficient optimization, it jointly incorporates spatial geometry, electro-acoustic characteristics, and acoustic propagation constraints to dynamically adapt digital filters for sound field correction. Experimental results demonstrate that the proposed method significantly improves sound field fidelity, source localization accuracy, and perceptual consistency across varying listener positions. It exhibits strong robustness to layout irregularities and dynamic listener movement, and is readily deployable in practical immersive audio systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Correcting sound-field errors in non-standard loudspeaker layouts
• Optimizing panning for adaptive sound-field correction
• Enabling accurate multichannel audio for non-stationary listening areas

Innovation

• Bayesian normalization adapts layouts for moving listeners
• Digital filters calibrate speakers without acoustic measurements
• Optimized panning creates virtual speakers in standard layouts