Active noise control with linear control source and sensor arrays for a noise barrier

Hart, Carl R.; Lau, Siu‐Kit

doi:10.1016/j.jsv.2011.08.016

Cited by 20 publications

(16 citation statements)

References 18 publications

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“…The applications of spherical loudspeakers arrays for ANC has been presented to extend ZoQ [234]. Following the idea of [234,235], several improvements were proposed including linear array [236], and RMT-based methods [237]. To design larger ZoQ, a method was proposed by introducing one and two secondary sources, which aimed at maximizing the area of the 10dB quiet zones [238].…”

Section: Zone-of-quiet Based Approachmentioning

confidence: 99%

A survey on active noise control in the past decade–Part II: Nonlinear systems

Yin

Lamare

et al. 2021

Signal Processing

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Section: Zone-of-quiet Based Approachmentioning

confidence: 99%

A survey on active noise control in the past decade–Part II: Nonlinear systems

Yin

Lamare

et al. 2021

Signal Processing

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“…To drive the control signal, the reference signal x (n) is designed to pass through an adaptive filter W (z) to minimize the error sensor signal e (n) [10,11]. The coefficients of W (z) are adapted using an algorithm called the least mean M-estimator (LMME) algorithm.…”

Section: Previously Applied Algorithmsmentioning

confidence: 99%

“…Block diagrams illustrated in Figure 1 show that ANC systems using the filtered-X (FX) least-mean-square algorithm require a reference signal x (n) for generating the control signal y (n). To drive the control signal, the reference signal x (n) is designed to pass through an adaptive filter W (z) to minimize the error sensor signal e (n) [10,11]. The coefficients of W (z) are adapted using an algorithm called the least mean M-estimator (LMME) algorithm.…”

Section: Previously Applied Algorithmsmentioning

confidence: 99%

Application of reinforcement learning for active noise control

Sabzevari¹,

Moavenian²

2017

Turk J Elec Eng & Comp Sci

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Active noise control (ANC) systems are used to reduce the sound noise level by generating antinoise signals.M-Estimators are widely employed in ANC systems for updating the adaptive FIR filter taps used as the system controller.Observing the state-of-the-art M-estimators design shows that there is a need for further improvements. In this paper, a feedback ANC based on the reinforcement learning (RL) method is proposed. The sensitivity of the constant parameter in the RL method is checked. The effectiveness of the proposed method is proven by comparing the results with previous feedforward studies through computer simulations.

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“…Hart and Lau [27] examined the performance of an active noise barrier, in which the height of barrier varied from 3 to 5 m, with three control sources and three error microphones linearly arranged above the top edge. The angles of control sources with the barrier changed from 0 • to 90 • , and the error microphones were always placed perpendicular to the control sources.…”

Section: Introductionmentioning

confidence: 99%

Suitability of Active Noise Barriers for Construction Sites

2020

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Barriers are increasingly used to protect the pedestrian and neighboring buildings from construction noise activities. This study aims to investigate the suitability of applying active noise control on barriers in a construction site to protect the street area and neighboring buildings. Transducers that are simulated in this work are close to the barrier, and their optimal positions are defined in such a way that the control system has the maximum performance at the neighboring areas close to the construction sites. To begin with, the suitable location of the control sources is found when the total squared pressure is minimized at the positions of noise receivers. The suitable location of the error sensors is, then, found when the control sources are fixed at the position of the previous step and the total squared pressure is minimized at the error sensors. The best location for the error sensors is defined when the maximum reduction is achieved in the target area. It is observed that suitable positions for the transducers depend on the location of target areas for noise control, the position of the noise source, and its operating frequency. In this investigation, a unique configuration is proposed for the transducers that achieves a comparable reduction both at the street area and the neighboring buildings, simultaneously. The results show that the active noise barrier with a height of 2.5 m can achieve an extra insertion loss in the street zone, varies from 9.3 to 16.4 dB (in comparison with passive noise barrier) when the distance of the noise source from the barrier changes in the range of 7 to 1 m, respectively. Those values are of the same order for the passive noise attenuation. Furthermore, similar results are achieved when attempting to cancel the shadow zone of a façade 15 m away from the barrier.

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Active noise control with linear control source and sensor arrays for a noise barrier

Cited by 20 publications

References 18 publications

A survey on active noise control in the past decade–Part II: Nonlinear systems

A survey on active noise control in the past decade–Part II: Nonlinear systems

Application of reinforcement learning for active noise control

Suitability of Active Noise Barriers for Construction Sites

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