Feedforward multichannel virtual-sensing active control of noise through an aperture: Analysis on causality and sensor-actuator constraints

Abstract

The multichannel implementation of the auxiliary-filter-based virtual-sensing (AF-VS) technique for active noise control applications is revisited and realized in the paper. Frequency-domain analysis based on random primary noise proves that the multichannel virtual-sensing active noise control (MVANC) technique can achieve optimal control at the desired virtual locations even if the signals at the physical and virtual microphones are not causally related. Further analysis on a number of sensor-actuator configurations shows that the MVANC technique achieves optimal control at the desired locations as long as the number of secondary sources does not exceed that of the physical error microphones. Furthermore, the simulations with measured transfer functions and real-time experiments conducted on a four-channel system validate the frequency domain analyses. V

Some Practical Acoustic Design and Typical Control Strategies for Multichannel Active Noise Control

“…Another problem that is worthy of study is the noise control at openings of an enclosure [19][20][21][22][23][24][25][26]. It has been found that more error sensors usually produce a better noise control performance.…”

A Momentum Two-Gradient Direction Algorithm with Variable Step Size Applied to Solve Practical Output Constraint Issue for Active Noise Control

“…Active noise control (ANC) is commonly employed to reduce ambient noise [1][2][3][4][5][6][7][8][9][10][11][12][13]. In a single-channel feedforward ANC system, a reference microphone and an error microphone are used to pick up the reference and error signals.…”

Real-Time Modelling of Observation Filter in the Remote Microphone Technique for an Active Noise Control Application

“…This limits its application to where the acoustic field remains relatively stationary throughout the active control period, such as in road noise ANC in automobile cabins, where head-tracking techniques were applied with the RMT to continuously update the location of the virtual error microphones due to head movement [8,9]. In cases where noise sources are time-varying and could arise from unknown directions, such as in the active control of noise through an open-aperture [10,11] or mobile phones [12], estimation performance will be degraded. While it was shown previously that the RMT estimation performance can be improved by reconstructing the correlation matrices (CMs) between microphones based on the superposition of CMs associated with its respective incoherent noise source [13], the reconstruction requires knowledge of the relative source strengths between these incoherent noise sources.…”