用SVD统一处理球面与线性麦克风阵列，提升声场重建精度。
A Unified SVD-Modal Solution for Sparse Sound Field Reconstruction with Hybrid Spherical-Linear Microphone Arrays
- 基于传递算子SVD分解，生成正交麦克风与声场模式
- 在混响环境下，能量图误差和角度误差均显著降低
- 适合需要高精度声场重建的智能音频系统
我们提出一种数据驱动的稀疏恢复框架，用于混合球面-线性麦克风阵列（hybrid spherical-linear microphone arrays），通过传递算子的奇异值分解（SVD）实现。SVD生成正交的麦克风与声场模式，在仅含球面麦克风阵列（SMA）时退化为球谐函数（SH），而引入线性麦克风阵列（LMA）后则产生超越SH的互补模式。模态分析显示，各频段均存在与SH的系统性偏差，证实了空间选择性的提升。在混响条件下实验表明，该方法在不同频率、距离和声源数量下，均显著降低了能量图失配与角度误差，优于仅使用SMA或直接拼接的方案。结果表明，SVD模态处理为混合阵列提供了原理一致且统一的稀疏声场重建方法。
原文摘要 · Abstract (English)
We propose a data-driven sparse recovery framework for hybrid spherical linear microphone arrays using singular value decomposition (SVD) of the transfer operator. The SVD yields orthogonal microphone and field modes, reducing to spherical harmonics (SH) in the SMA-only case, while incorporating LMAs introduces complementary modes beyond SH. Modal analysis reveals consistent divergence from SH across frequency, confirming the improved spatial selectivity. Experiments in reverberant conditions show reduced energy-map mismatch and angular error across frequency, distance, and source count, outperforming SMA-only and direct concatenation. The results demonstrate that SVD-modal processing provides a principled and unified treatment of hybrid arrays for robust sparse sound-field reconstruction.
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