An Alternative Hybrid Time-Frequency Domain Approach Based on Fast Iterative Shrinkage-Thresholding Algorithm for Rotating Acoustic Source Identification

Abstract

An alternative hybrid time-frequency domain approach based on the fast iterative shrinkagethresholding algorithm (FISTA) is presented for rotating acoustic source identification. This approach is similar to the existing approaches based on the deconvolution approach for the mapping of acoustic sources (DAMAS) and non-negative least squares (NNLS) without the sample interpolation of sound pressure signal in the time domain. The acoustic source is first identified using the time-domain tracking Delay and Sum (DAS) method. The Frequency-domain deconvolution is then performed to attenuate the sidelobes and improve the spatial resolution. The simulation and experimental results indicate that the proposed approach is robust to the sampling rate and enjoys the good spatial convergence and resolution, effective sidelobe suppression, accurate quantification, as well as high computational efficiency. In general, at a low time-domain sampling rate, the proposed approach outperforms the DAMAS-or NNLS-based hybrid time-frequency domain approaches without the sample interpolation of sound pressure signal in the time domain. INDEX TERMS Rotating acoustic source identification, hybrid time-frequency domain approach, fast iterative shrinkage-thresholding algorithm.

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

“…However, most of the studies are theoretical and simulation-based as they involve the development of signal processing techniques for overcoming sensor noise, mutual coupling, obtaining accurate DOA estimation in the presence of fewer snapshots, operation in harsh and low signal-to-noise-ratio (SNR) conditions etc [22]- [24]. Another portion of research papers is devoted to the hardware design of microphone arrays [25]- [28].An outdoor sound source localization system wherein a microphone array was fitted to a drone was demonstrated in [29].A huge microphone array with 512 microphones was designed and demonstrated in [30]. Similarly, a 1020-node microphone array was designed and tested successfully [31].…”

An Improved Time-Domain Inverse Technique for Localization and Quantification of Rotating Sound Sources

“…Pan et al [16] developed a correction method for the TRB to restrain the singular problem in identifying rotating sources with the non-uniform directivity. In recent years, deconvolution algorithms were subsequently used to improve the spatial resolution of the TRB [17][18][19][20][21]. Nevertheless, the TRB utilizes the advanced time method to calculate the receiver time based on the source time, which causes inconsistency between the calculated receiver time and the sampling time.…”

Theoretical analysis of the DAMAS algorithm and efficient implementation of the covariance matrix fitting method for large-scale problems

“…Several methods, most of them numerically more efficient than the DAMAS algorithm, have been proposed since 2006 (see [2] for a review of such methods), and are frequently compared with it, one recurring remark being the computational effort that the DAMAS algorithm requires [3][4][5][6][7][8][9][10][11][12] . Among these methods, several variants of the DAMAS algorithm with improved numerical efficiency have been proposed, based on the formulation of the problem in the Fourier domain [13], or pruning of the grid points that will not be involved in the final source map either directly [10,11], or similarly in a wavelet basis [8].…”