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The Non-Synchronous Measurements is particularly effective at expanding the operating frequency range of a single microphone array. The completed cross-spectral matrix of the synthetic array, when combined with high-resolution imaging algorithms, can yield accurate sound source identification results. However, when matrix completion algorithms are applied to large synthetic arrays, their computational complexity increases significantly. Furthermore, existing integrations with high-resolution imaging algorithms often encounter difficulties in the presence of coherent sound sources. To address this problem, this paper introduces the accelerated gradient descent algorithm for the covariance matrix fitting by orthogonal least squares. The matrix completion model for non-synchronous measurements is initially simplified through matrix decomposition, thereby enabling the rapid completion of the cross-spectral matrix using the accelerated gradient descent algorithm. This is then followed by the application of the covariance matrix fitting by orthogonal least squares to achieve quick and precise identification of coherent sound sources employing the completed cross-spectral matrix. The performance of the algorithm is evaluated using numerical simulations and validated through loudspeaker experiments in an anechoic chamber. The results from these simulations and experiments reveal that the proposed algorithm not only improves matrix completion performance on large synthetic arrays but also accurately identifies the locations and correlation coefficients of coherent sound sources. [ABSTRACT FROM AUTHOR]

Noise source localization is considered a key technology for the low-noise design of machinery and equipment. The spatial resolution of conventional beamforming localization methods is effectively improved by the fast iterative shrinkage thresholding algorithm(FISTA). The application of FISTA for sound source localization in reverberant environments is limited by the selection of reflection parameters. In this paper, FISTA extends to FISTA-Net and is proposed to solve the problem of adaptive fitting of reflection parameters to improve the performance of sound source localization in a reverberant environment. In FISTA-Net, the network model can be established by expanding and truncating the FISTA algorithm based on a fixed number of iterations. The parameter iteration steps, shrinkage thresholds and update operators are updated in the FISTA-Net model by training. A series of simulations are conducted for various frequencies, reverberation times, and signal-to-noise ratios and compared to several de-reverberation algorithms to evaluate the performance of the proposed algorithm. The loudspeaker source localization experiment was conducted in a closed room to verify the effectiveness of FISTA-Net in real-world scenarios. [ABSTRACT FROM AUTHOR]

Transfer learning techniques have been extensively developed for the intelligent diagnosis of rotating machinery as a critical and valuable tool dedicated to minimizing the distributional discrepancies between different working conditions of the machine. However, conditional probability information about fault classes and the geometric features of data distribution is rarely considered in traditional distance metrics, invalidating cross-domain diagnostic models when faced with significant distributional discrepancies. To address these issues, a new cross-domain diagnostic algorithm is proposed via joint conditional Wasserstein distance matching. First, the conditional Bures–Wasserstein distance is constructed based on the second-order statistic cross-covariance operator, approximating the distributions in the source and target domains while constraining the geometry. Then, to avoid losing first-order fault data information, the conditional probability 1-Wasserstein distance is embedded to construct a joint distance adaptation. The entropy loss is introduced into the training process to build reliable pseudo labels for the target domain samples. In the proposed method, the samples and label features of different domains are mapped to the reproducing kernel Hilbert space (RKHS), and the Feature Extractor and Classifier modules of the model are jointly optimized to obtain a more robust diagnostic model. The proposed cross-domain diagnostic model is experimentally validated on bearing and gearbox datasets under variable loads and speeds with significant diagnostic performance compared to existing transfer models. [ABSTRACT FROM AUTHOR]

The Modal Composition Beamforming (MCB) method can quickly achieve the localization of fast-rotating sound sources; however, its low resolution and unclear applied conditions severely limit its industrial application. This study aims to investigate the MCB-based high-resolution localization method for multiple rotating sound sources and apply it to the localization and identification of axial-fan blade-noise. In this paper, the Variational Bayesian Approximation (VBA) and the Subspace Variational Bayesian Approximation (SVB) methods are used to solve the MCB-based rotating sound power propagation (RSP) model, denoted as RSP-VBA and RSP-SVB, respectively. The effectiveness of the proposed RSP-VBA and RSP-SVB are experimentally validated for the first time. Compared to the conventional MCB method, the resolution is significantly improved, and the proposed high-resolution methods are even faster than the classical Rotating Source Identifier (ROSI) method in most conditions. More importantly, the applied condition of the MCB, RSP-VBA, and RSP-SVB methods is given and verified by using three evaluation indicators. Then a schematic of the applied condition with examples is provided. With the guide of the applied conditions, the RSP-VBA and RSP-SVB methods are applied to the blade-noise localization of various multiblade high-speed axial fans. [ABSTRACT FROM AUTHOR]