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.
Highlights
Beamforming [1,2,3] based on a microphone array has become a popular sound source identification technology for aircraft [4], express train [5], wind turbine [6], automobile [7], etc
In 2015, on the basis of the iterative shrinkage-thresholding algorithm (ISTA) [14] and the fast iterative shrinkage-thresholding algorithm (FISTA) [15], which are used to solve the inverse problem in the image processing, Lylloff et al [16] proposed Fourier transform- (FFT-)FISTA deconvolution beamforming for sound source identification
Zhao et al [17] recently proposed the smoothing fast iterative shrinkage-threshold algorithm (SFISTA), which enjoys the advantage of quickly processing the large-scale problems in the compressive sensing framework
Summary
Beamforming [1,2,3] based on a microphone array has become a popular sound source identification technology for aircraft [4], express train [5], wind turbine [6], automobile [7], etc. Is makes it difficult for FISTA to introduce sparse constraints directly and explicitly To overcome this difficulty, Zhao et al [17] recently proposed the smoothing fast iterative shrinkage-threshold algorithm (SFISTA), which enjoys the advantage of quickly processing the large-scale problems in the compressive sensing framework. Several similar deconvolution beamforming techniques successfully include the sparse distribution constraint of sound source, such as sparsity constrained DAMAS (SC-DAMAS) [18], robust super-resolution approach with sparsity constraint (SCRDAMAS) [19], and orthogonal matching pursuit DAMAS. [16, 17], this paper proposes a SFISTA deconvolution beamforming, which includes the sparsity constraint that the main sound sources are usually sparsely distributed. Compared to the SC-DAMAS and FISTA, the proposed approach enjoys faster convergence speed, higher quantification accuracy and computational efficiency, and more insensitivity to measurement noise.
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