Hybrid deconvolution method based on mode composition beamforming for separating sound sources with different motion modes

Abstract

The presence of sound sources that rotate can cause interference with stationary acoustic imaging, especially if the intensity of the rotating sound source is strong. Vice versa, stationary sound sources can also interfere with rotational acoustic imaging. Current algorithms for separating and locating rotating and stationary sound sources have problems such as weak separation capabilities, complex calculations, and limited array layouts. To address these challenges more effectively, a frequency-domain hybrid deconvolution method based on modal composition beamforming is proposed to separate and localize rotating and stationary sound sources. This algorithm operates within the frequency domain and is not restricted by array layout or the tonal single-frequency nature of sound sources. First, the point spread function and cross-point spread function matrices of the array are derived based on the modal transfer function. Then, a system of linear equations is constructed to separate rotating and stationary sound sources. Finally, the sound source separation and localization problems are solved through Gaussian Seidel and sparse-constrained deconvolution, respectively. Compared with the virtual rotating array method combined with matrix completion, simulation and experimental results demonstrate that this method can accurately locate and separate rotating and stationary sound sources, even when there is a substantial difference in intensity between rotating and stationary sound sources.