Adaptive Linearly Constrained Minimum Variance Framework for Volumetric Active Noise Control

TL;DR

This paper presents a flexible, adaptive LCMV ANC framework in the time domain for volumetric noise control, enabling targeted spatial noise reduction with proven effectiveness through simulations and experiments.

Contribution

It introduces a novel time domain LCMV ANC formulation with an adaptive FxLMS algorithm for spatially selective broadband noise control.

Findings

Effective noise reduction at targeted locations

Enhanced spatial control flexibility

Successful real-world experimental validation

Abstract

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.