Improving the Sound Source Identification Performance of Sparsity Constrained Deconvolution Beamforming Utilizing SFISTA

Abstract

In this paper, an alternative sparsity constrained deconvolution beamforming utilizing the smoothing fast iterative shrinkage-thresholding algorithm (SFISTA) is proposed for sound source identification. Theoretical background and solving procedures are introduced. The influence of SFISTA regularization and smoothing parameters on the sound source identification performance is analyzed, and the recommended values of the parameters are obtained for the presented cases. Compared with the sparsity constrained deconvolution approach for the mapping of acoustic sources (SC-DAMAS) and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed SFISTA with appropriate regularization and smoothing parameters has faster convergence speed, higher quantification accuracy and computational efficiency, and more insensitivity to measurement noise.