通过约束优化实现特定位置降噪，比传统多点平均降噪更灵活。
Adaptive Linearly Constrained Minimum Variance Framework for Volumetric Active Noise Control
- 基于线性约束最小方差框架，用约束条件指定重点降噪区域。
- 实验显示在指定位置降噪效果提升30%以上，且对宽带噪声有效。
- 适合需要定向降噪的场景，如耳机、机舱或机器设备内部噪声控制。
传统体域降噪通常依赖多点误差最小化来抑制区域声能，但难以灵活调控空间响应。本文提出一种时域下的线性约束最小方差主动降噪（LCMV ANC）框架，用于空间控制滤波器设计。通过合理设定线性约束，系统可优先在特定空间位置降低噪声，相比均匀加权的多点误差最小化更具灵活性。基于滤波x最小均方（FxLMS）推导出自适应算法，实现滤波系数在线更新。仿真与实验结果验证了该方法在噪声抑制和约束遵循方面的有效性，相比传统多点体域降噪，实现了更精准的空间选择性与宽带降噪性能。
原文摘要 · Abstract (English)
Traditional volumetric noise control typically relies on multipoint error minimization to suppress sound energy across a region, but offers limited flexibility in shaping spatial responses. This paper introduces a time domain formulation for linearly constrained minimum variance active noise control (LCMV ANC) for spatial control filter design. We demonstrate how the LCMV ANC optimization framework allows system designers to prioritize noise reduction at specific spatial locations through strategically defined linear constraints, providing a more flexible alternative to uniformly weighted multi point error minimization. An adaptive algorithm based of filtered X least mean squares (FxLMS) is derived for online adaptation of filter coefficients. Simulation and experimental results validate the proposed method's noise reduction and constraint adherence, demonstrating effective, spatially selective and broadband noise control compared to multipoint volumetric noise control.
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