Adaptive Linearly Constrained Minimum Variance Volumetric Active Noise Control

2025-07-08

AI Summary

Traditional active noise control (ANC) relies on multi-point equally weighted error minimization, limiting flexibility in spatial sound field shaping. This work proposes a time-domain LCMV (Linearly Constrained Minimum Variance)-based active control method, explicitly modeling spatially selective noise reduction requirements as linear constraints to overcome the inherent limitation of uniform weighting. By integrating an enhanced filtered-x LMS (FxLMS) adaptive algorithm, the approach enables real-time filter coefficient updates while dynamically satisfying the imposed constraints. Simulation and experimental results demonstrate that the method significantly enhances preferential noise suppression within target regions under broadband excitation, achieves high constraint adherence accuracy, and offers superior spatial selectivity and sound field control degrees of freedom compared to conventional ANC approaches. The framework provides a scalable optimization foundation for three-dimensional directional noise reduction.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 multipoint error minimization. An adaptive algorithm based on 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.

Problem

• Enhances spatial noise control flexibility via LCMV ANC
• Prioritizes noise reduction at specific locations with constraints
• Validates adaptive FxLMS for selective broadband noise control

Innovation

• LCMV ANC for spatial filter design
• Adaptive FxLMS algorithm online
• Spatially selective noise control