Accurate source identification is key to solving complex flow-induced noise problems, and sources often have non-uniform radiation patterns. At the same time, traditional methods have many application limitations, including the need for prior knowledge of the sound field, as well as inability to apply to coherent sources or coexisting multipoles. A multipole transfer matrix model-based sparse Bayesian learning approach for sound source identification (MT-SBL) is proposed. In this algorithm, the transfer relationship for the multipole radiation patterns of sound sources is established. Then, the signal sparse reconstruction is transformed into an iterative updating problem of the feature parameters under a sparse Bayesian learning (SBL) framework, which leads to the accurate identification of multipole sources. Numerical simulations and experiments on electric speakers in an anechoic environment validate the proposed algorithm. Compared with the existing methods, the algorithm is more advantageous in terms of accuracy, resolution, and computational efficiency. Further experiments were conducted on the identification of supersonic jet noise sources, verifying the effectiveness of the method for identifying strongly directional sound sources.

中文翻译：

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基于多极传递矩阵模型的稀疏贝叶斯学习声源识别方法

准确的源识别是解决复杂的流引起的噪声问题的关键，并且源通常具有不均匀的辐射模式。同时，传统方法存在许多应用限制，包括需要声场的先验知识，以及无法应用于相干源或多极共存。提出了一种基于多极传递矩阵模型的稀疏贝叶斯学习声源识别方法（MT-SBL）。该算法建立了声源多极辐射方向图的传递关系。然后，将信号稀疏重构转化为稀疏贝叶斯学习（SBL）框架下特征参数的迭代更新问题，从而实现多极源的准确识别。消声环境中电动扬声器的数值模拟和实验验证了所提出的算法。与现有方法相比，该算法在精度、分辨率和计算效率方面更具优势。进一步对超音速射流噪声源识别进行实验，验证了强方向性声源识别方法的有效性。