用球形与线性麦克风阵列协同重构声场，提升复杂环境下的空间精度。
Hierarchical Sparse Sound Field Reconstruction with Spherical and Linear Microphone Arrays
- 分两阶段融合球形与线性阵列数据，主阵列估测+辅阵列补强
- 在混响环境下重建能量图的精度显著优于仅用球形阵列的方法
- 适合需要高保真声场建模的智能音箱、虚拟现实等场景
球形麦克风阵列（SMAs）广泛用于声场分析，稀疏恢复（SR）技术可通过将声场建模为少数主导平面波的稀疏叠加，显著提升其空间分辨率。然而，SMAs的空间分辨率受其球谐阶数限制，且在混响环境中性能常下降。本文提出一种两阶段稀疏恢复框架，结合中心SMAs与四个环绕线性麦克风阵列（LMAs）的观测数据。核心思想是利用SMAs作为主估计器，LMAs提供空间互补性以精细化残差。仿真结果表明，在不同混响条件下，所提SMA-LMA方法在空间能量图重构上显著优于仅用SMA或直接一步联合处理的方法。该框架有效提升了复杂声学环境中的空间保真度与鲁棒性。
原文摘要 · Abstract (English)
Spherical microphone arrays (SMAs) are widely used for sound field analysis, and sparse recovery (SR) techniques can significantly enhance their spatial resolution by modeling the sound field as a sparse superposition of dominant plane waves. However, the spatial resolution of SMAs is fundamentally limited by their spherical harmonic order, and their performance often degrades in reverberant environments. This paper proposes a two-stage SR framework with residue refinement that integrates observations from a central SMA and four surrounding linear microphone arrays (LMAs). The core idea is to exploit complementary spatial characteristics by treating the SMA as a primary estimator and the LMAs as a spatially complementary refiner. Simulation results demonstrate that the proposed SMA-LMA method significantly enhances spatial energy map reconstruction under varying reverberation conditions, compared to both SMA-only and direct one-step joint processing. These results demonstrate the effectiveness of the proposed framework in enhancing spatial fidelity and robustness in complex acoustic environments.
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