Search

Acoustic source reconstruction techniques are an essential technical basis for noise source identification and fault diagnosis. How to computationally efficiently obtain the accurate estimation of acoustic source location and strength remains a fundamental challenge for acoustic source reconstruction techniques. To accommodate the needs of processing complex-valued signals, complex approximate message passing algorithm (CAMP) is considered. Benefiting from the Onsager correction term, the reconstruction performance and convergence speed of CAMP are enhanced. The computational complexity of CAMP is significantly reduced by avoiding matrix inversion in each iteration. Therefore, a sparse Equivalent Source Method (ESM) based on CAMP is proposed in this paper and named CAMP-ESM for achieving acoustic source reconstruction with increased accuracy and computational efficiency. Simulation results show in the mid-high frequency range, CAMP-ESM achieves the best reconstruction results in the shortest time compared to other ℓ 1 -based ESM. In addition, the proposed method achieves a good balance between reconstruction accuracy and computational efficiency compared to other state-of-the-art ESM methods. Two experiments verified that CAMP-ESM can accurately localize the acoustic source and quantify the acoustic power. [ABSTRACT FROM AUTHOR]

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]

Fan noise is a predominant noise component of the aero-engine, which consists of tonal noise and broadband noise. Duct mode calculation of random broadband noise is more complex than that of tonal noise. A probabilistic approach for duct acoustic mode identification is proposed to identify duct modes of broadband fan noise with high efficiency and accuracy. The inverse problem of mode identification is represented by a Bayesian framework based on a Gaussian-scale mixture prior model. Computational complexity and consuming time are remarkably decreased by approximating the Gaussian likelihood through a surrogate function. The block coordinate descent algorithm in the majorization–minimization framework is adopted for the indirect and iterative calculation of the mode coefficients. Simulations and experiments have verified the high computational efficiency afforded by the method, which provides more accurate results for duct mode identification of broadband noise, removes the influence of interfering modes on the recognition results, and allows better observation of the characteristics of the mode in a wide range of frequencies and rotating speeds. [ABSTRACT FROM AUTHOR]