自适应声场校正，让多声道音响在非标准布局下也能精准还原声音。
Optimized Loudspeaker Panning for Adaptive Sound-Field Correction and Non-stationary Listening Areas
- 用贝叶斯方法动态估计听众位置，实时调整声场参数。
- 无需测量，通过数字滤波器使不同音箱输出一致音质。
- 优化音源分配策略，支持移动听感的稳定沉浸体验。
多声道音频系统通常采用标准化扬声器布局，但在非受控环境中，扬声器数量、位置及听音区常不固定，导致声场失真，影响音色、定位与清晰度。本文提出贝叶斯扬声器归一化与内容声像优化方法，基于扬声器-听者方向的共轭先验分布，动态更新非稳态听音区域估计；无需声学测量，数字滤波器即可将各扬声器响应适配至目标参考声场。频率域声像系数在空间、电学与声学约束下，以灵敏度与效率为目标进行优化，经归一化与声像定位后的扬声器可构建标准布局中的虚拟扬声器，实现精确多声道再现。实验验证了贝叶斯自适应与声像优化在实际应用中的鲁棒性。
原文摘要 · Abstract (English)
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.
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