Kernel-Interpolation-Based Filtered-X Least Mean Square For Spatial Active Noise Control In Time Domain

Time-domain spatial active noise control (ANC) algorithms based on kernel interpolation of a sound field are proposed. Spatial ANC aims to suppress a primary noise field in a region by synthesizing an anti-noise field using secondary loudspeakers. In contrast to the multipoint pressure control by conventional ANC methods, it is necessary to estimate a sound field from microphone measurements at discrete positions. A promising approach is the kernel-interpolation-based spatial ANC method, which allows for flexible array configurations. However, most of the current methods are formulated only in the frequency domain, which cannot handle broadband noise with straightforward implementation. We formulate two time-domain spatial ANC algorithms based on kernel interpolation using a spatial interpolation filter defined by the kernel function. Numerical simulation results indicate that our filtered-x least-mean-square-based algorithms are effective for reducing nonstationary broadband noise in a region, as compared with multipoint pressure control.