The Effectiveness of Least Mean Squared-Based Adaptive Algorithms for Active Noise Control System in a Small Confined Space

Abstract

Active noise control (ANC) is a technique applied to eliminate an unwanted sound by superposing a signal of equal amplitude and opposite phase, sometimes defined as an anti-noise signal, computed through an adaptive algorithm. The study described herein aims to evaluate and compare the performance of some of the most popular algorithms based on the least mean squares (LMS) approach applied to a multichannel active noise control system in a small, enclosed space. The comparison is conducted through an experimental evaluation of the ANC algorithms’ performance, carried out on a tractor cabin in a hemi-anechoic chamber, generating the unwanted sound field using a dodecahedron sound source placed outside the enclosure, emitting narrowband and broadband signals. The experimental analysis and the comparison with the results obtained in a free field condition have made it possible to show certain practical limitations when implementing the algorithms. The results show that the feed-forward systems allow for greater stability, avoiding the acoustic feedback from the control loudspeakers to the reference microphone when this is outside the cabin, while the feedback system is the slowest configuration to converge, requiring an internal modeling of the reference signal. With random signals, the feed-forward systems concentrate their performance in the range above 500 Hz, while the feedback system becomes ineffective.

Improving Insertion Loss of Sonic Crystal Active Noise Barrier by Reinforcement Learning and Finite Difference Time Domain Simulations

“…These systems operate by using strategically placed microphones to capture incoming noise and speakers to emit sound waves that are precisely out of phase with the noise, leading to cancellation [10]. ANC has been successfully applied in various domains, such as automotive [11], [12] and industrial settings [13], where reducing noise is crucial. However, the effectiveness of ANC systems relies heavily on the accuracy of noise prediction models and the sophistication of the control algorithms used to generate the anti-phase sound waves [14].…”