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Sound source visualization has been widely used in many scenarios such as aerospace, industrial production, and urban management. Sound source localization technology plays an essential role in the realization of sound source visualization. The imaging results and calculation cost are the primary considerations in the problem of sound source localization. This article proposes a subspace iteration integrated variational Bayesian (SVB) method to realize adaptive imaging of different sound sources. First, the proposed variational Bayesian (VB) method is based on total variation (TV) prior to balance the sparsity and smoothness of the imaging results. Second, the subspace optimization method in the probability measure space is integrated into the proposed SVB method to solve the involved ill-posed inverse problem. The proposed SVB method can significantly improve the calculation speed, especially for large-scale inverse problems. Finally, the speed and robustness of the proposed SVB method can be demonstrated according to the extensive results of simulation and experimental validation. [ABSTRACT FROM AUTHOR]

The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SF-MCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green’s function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation ($T_{20}=174.30\,\,\text {ms}$). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise. [ABSTRACT FROM AUTHOR]

This article proposes a variational Bayesian (VB) inference based on the nonsynchronous array measurement (NAM) (VB-NAM) method in order to obtain the fast and robust localization of low-frequency acoustic sources. To enlarge the aperture size compared with the prototype array, the NAM is performed to measure the acoustic pressures with low-frequency based on the forward power propagation model. The implementation of the NAM can be reformulated into a cross-spectral matrix (CSM) completion problem. Then, to solve the inverse problem of the NAM power propagation model, the VB inference based on the Student-t priors and Kullback–Leibler (KL) divergence optimization is proposed. The advantages of the proposed VB-NAM benefit from the optimization of matrix inversion and adaptive estimation of regularization parameters. The contribution of the adaptive parameter evaluation is to reduce the impact of multiple interferences (such as additive noise and the matrix completion error) in NAM. Finally, both simulations at 800 Hz and experimental results at 1000 Hz are presented to show the validation of the proposed VB-NAM method, even under the anisotropic Gaussian noise conditions. Algorithm performance and iteration process are analyzed to demonstrate the efficiency and robustness. [ABSTRACT FROM AUTHOR]

The localization of cyclostationary sound sources is important for rotating machinery noise source identification and fault diagnosis. Cyclostationary sound sources are a special class of nonstationary sound sources, which exhibit periodic changes in statistics. Most of the conventional sound source identification methods are based on the stationary sound source assumption, which do not consider the cyclostationarity of the radiated sound field by rotating machinery. Thus, they are hard to localize the acoustic sources with different cyclic frequencies. In this paper, a novel technique of localizing the cyclostationary acoustic sources with different cyclic frequencies is proposed. First, the cyclostationary conventional beamforming (CSCBF) method is deviated. In CSCBF, the cyclic-cross-spectral matrix (CCSM) is defined considering all the cross cyclic spectral correlation (CSC) between different microphones in the array. The left steering vector and right steering vector are constructed to focus the CCSM on each scanning grid point at a given combination of cyclic frequency and spectral frequency. Then, the high resolution cyclostationary beamforming (HR-CSBF) is further developed to improve the spatial resolution of CSCBF. In HR-CSBF, two linear equations according to the property of CSC is established as the acoustic inverse problems. With the sparse distribution of the cyclostationary sources in the space as the prior knowledge, the iterated Bayesian focusing (IBF) is utilized to obtain a robust and sparse solution in HR-CSBF. Numerical simulations and experiments are conducted to validate the proposed methods. To explore the potential applications of the proposed methods, the localization of high pressure pump (HPP) and rolling bearing with outer-race failure are also used to evaluate the effectiveness of the proposed methods. It turns out that both CSCBF and HR-CSBF can localize the cyclostationary sources with different cyclic frequencies. Compared with CSCBF, HR-CSBF can achieve a higher spatial resolution in the obtained acoustic map. [ABSTRACT FROM AUTHOR]

The Non-Synchronous Measurements is an efficient technique to improve the acoustic image of conventional beamforming for sound source localization. It is unable to quickly reveal the sound sources of the large machinery which requires a large synthetic array with many microphones to capture enough sound pressure data. In this paper, four Non-Synchronous Measurement algorithms with improved computational efficiency are demonstrated. They are implemented by combining the Alternative Direction Method of Multipliers (ADMM) method with different randomization-based acceleration strategies. The new algorithms are developed firstly by (1) using Randomized Singular Value Decomposition (RSVD) to construct the projection basis, and then by (2) migrating the computation platform from the Central Processing Unit to the Graphics Processing Unit for the Cross-Spectral Matrix completion steps, (3) accelerating the soft threshold shrinkage operator with RSVD, or (4) replacing the soft threshold shrinkage with RSVD-enhanced optimal shrinkage in the Cross-Spectral Matrix completion steps. Numerical simulations are carried out under different conditions, including the number of sequential measurement positions, the step length between two sequential measurement positions, the frequency of the sound sources, and the Signal-to-Noise Ratio of the sound pressure signals measured by the microphones. The sound source localization performance of the improved algorithms is assessed by examining their acoustic maps, and the computational performance of these algorithms is compared through their Cross-Spectral Matrix reconstruction errors and time consumption. • Four accelerated Non-Synchronous Measurement algorithms are proposed. • Proposed algorithms are based on the Randomized Singular Value Decomposition. • The matrix completion accuracy of the original algorithm are optimized. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of rotating source localization to identify the axial fan noise with high spatial resolution methods. It exploits the on time-domain de-Doppler, and sparsity-based inversion and the convolution model to approximate the power propagation model of the rotating source. This work is also an extension of the previous paper of authors. But the focus of this study is the experimental investigation and application of actual data. To evaluate the performance of the proposed algorithm, a vortex generator is attached to the fan blade, creating discontinuities in the blade to simulate axial fan blade defects (cracks, holes, dust accumulations, etc.). The experimental data are shared as a data benchmark to promote further studies in the field of rotating acoustic localization. Compared with the state-of-the-art rotating Beamforming method (ROSI), the acoustic imaging results at various working frequencies for three different axial fans with different rotating speeds (RPM) are accurately identified and sparsely distributed. In addition, the power propagation model derived in the previous paper can be interpreted by a convolutional approximation model in this paper, and this technique can help to reduce the computational costs of proposed methods for real applications. The accuracy of the aerodynamic noise positions estimated by the proposed method are further proved to match well with the aerodynamic mechanism. • A practical implementation for the rotating sound sources localization in industrial axial fans is presented. • Two types of vortex generators (VGs) are attached to the blade, simulating the blade defects. • An explanation for the power propagation model based on the convolutional approximation is presented. • A data benchmark is established for peers to compare various rotating sound source localization methods. [ABSTRACT FROM AUTHOR]