Bhan Lam

Open loop active control of noise through open windows

Noise propagating through open windows raises one major concern of livability in metropolises, where residential buildings and arterial roads are densely constructed. Active noise control (ANC) is a possible technique to improve the current situation. Usually, ANC systems adopt the closed-loop control, where the quietness after control is continuously monitored by error microphones. However, ANC systems that attenuate noise propagating through open windows should ideally avoid the use of error microphones for the ease of implementation and maintenance. Furthermore, as open windows are commonly larger than the wavelength of the incident noise, multichannel ANC systems are necessary. Therefore, they result in computational burdens that cannot be handled by low-cost hardware platforms. This paper formulates the ANC system for open windows as a spatial sampling and reconstruction problem, and demonstrates that controlling noise propagating through open windows leads to global reduction of noise levels in the ...

Understanding multiple-input multiple-output active noise control from a perspective of sampling and reconstruction

This paper formulates the multiple-input multiple- output active noise control as a spatial sampling and reconstruction problem. With the proposed formulation, the inputs from the reference microphones and the outputs of the antinoise sources are regarded as spatial samples. We show that the proposed formulation is general and can unify the existing control strategies. Three control strategies, for instance, are derived from the proposed formulation and linked to different cost functions in the practical implementation. Finally, simulation results are presented to verify the effectiveness of our analysis.This paper develops a novel approach to adaptive active noise control based on the theory of Bayesian estimation. Control system parameters are considered as statistical variables and a formulation for the joint probability density function of them is derived. An optimal solution for the system parameters is then calculated through maximizing the density function. An efficient adaptive algorithm for iterative calculation of the optimal parameters is proposed. It is shown that the well- known FxLMS algorithm can be derived as a special case of the proposed algorithm, where the noise to be canceled is a white Gaussian process. Simulation results verify the preference of the proposed system to the traditional active noise control systems in terms of steady-state performance and convergence rate. It is also shown that the preference of the proposed system is much more evident when the noise to be canceled is not white. Finally, a successful implementation of the proposed system in an experimental acoustic duct is reported.