A Unified SVD-Modal Solution for Sparse Sound Field Reconstruction with Hybrid Spherical-Linear Microphone Arrays

TL;DR

This paper introduces a unified SVD-based modal framework for sparse sound field reconstruction using hybrid spherical-linear microphone arrays, improving spatial accuracy and robustness over existing methods.

Contribution

It presents a novel SVD-modal approach that effectively combines spherical and linear array modes for enhanced sound field reconstruction.

Findings

Reduced energy-map mismatch in reverberant environments

Lower angular error across frequency and source count

Outperforms SMA-only and direct concatenation methods

Abstract

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.