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.

翻译：环绕声系统通常按照标准化布局配置扬声器以实现多声道音频重放。然而在控制较弱的实际环境中，扬声器的数量、摆放位置以及听音位置/区域往往存在差异。这些偏离标准布局的情况会引入声场误差，导致音频内容重放时的音色、声像定位和清晰度下降。本研究提出了贝叶斯扬声器归一化与内容声像定位优化两种声场校正方法。基于共轭先验分布的扬声器-听者方向模型可更新非稳态听音位置的布局估计；数字滤波器将扬声器声学响应自适应调整至估计听音区域的共同参考目标，且无需声学测量。随后通过灵敏度/效率目标函数在空间、电学和声学域约束下优化频域声像定位系数；经归一化与声像定位处理的扬声器可在标准化布局中形成虚拟扬声器阵列，实现精确的多声道重放。实验探究了贝叶斯自适应方法与声像定位优化技术在实际应用中的鲁棒性。