Computation-efficient Virtual Sensing Approach with Multichannel Adjoint Least Mean Square Algorithm

TL;DR

This paper introduces a computationally efficient multichannel virtual sensing active noise control system using the adjoint LMS algorithm, achieving similar noise reduction with lower complexity.

Contribution

It proposes a novel MVANC system with the MCALMS algorithm, significantly reducing computational complexity compared to traditional methods.

Findings

Enhanced computational efficiency demonstrated through analysis.

Comparable noise reduction performance to conventional algorithms.

Insights into tuning noise effects on system performance.

Abstract

Multichannel active noise control (ANC) systems are designed to create a large zone of quietness (ZoQ) around the error microphones, however, the placement of these microphones often presents challenges due to physical limitations. Virtual sensing technique that effectively suppresses the noise far from the physical error microphones is one of the most promising solutions. Nevertheless, the conventional multichannel virtual sensing ANC (MVANC) system based on the multichannel filtered reference least mean square (MCFxLMS) algorithm often suffers from high computational complexity. This paper proposes a feedforward MVANC system that incorporates the multichannel adjoint least mean square (MCALMS) algorithm to overcome these limitations effectively. Computational analysis demonstrates the improvement of computational efficiency and numerical simulations exhibit comparable noise reduction performance at virtual locations compared to the conventional MCFxLMS algorithm. Additionally, the effects of varied tuning noises on system performance are also investigated, providing insightful findings on optimizing MVANC systems.