Low-frequency sound source localization and identification with spherical microphone arrays extrapolation method

Abstract

Beamforming technology with spherical microphone arrays (SMAs) is often applied for localizing and identifying noise sources in airplane cabins, automobiles, and submarines. The performance of a SMA signal processing algorithm depends on its physical characteristics, especially the array aperture. The array aperture limits the frequency range of its application, and the small aperture leads to weak performance at low frequencies. In this paper, a large-aperture virtual SMA is obtained through the virtual SMA extrapolation method. Because the radius of the virtual SMA is larger than that of the actual SMA, an approximate low-frequency signal can be obtained, which may improve the localization effect of the low-frequency noise source of the SMA. Firstly, the paper introduces the extrapolation method of SMA, and through the discussion of several typical parameters such as envelope parameters, SMA aperture and signal-to-noise ratio (SNR), the application scope and conditions of SMA extrapolation method are given. In addition, this paper introduces compressed sensing technology (CS) into the calculation process of virtual SMA extrapolation to improve the accuracy of virtual SMA element data. The generalized inverse beamforming (GIB) algorithm is then used to locate and identify noise sources and verify the benefits of the virtual SMA. Simulation and experimental results show that the virtual SMA can locate and identify noise sources with high spatial resolution in the low frequency range.