Deep Learning Approaches for Microphone Arrays in Acoustic Testing

麦克风阵列方法是声源定位和表征的一种成熟方法。通过适当的信号处理，不同声源的贡献可以在空间上映射。然而，较低的动态范围通常使得很难将映射的源贡献分配给它们的原因并确定源机制。由于这个原因，已经开发了许多不同的方法，这些方法在精度、计算量和抗干扰鲁棒性方面差异很大。根据测量任务的不同，并不总是能获得令人满意的结果。因此，改进方法的发展是可取的。该项目的主要目标是建立深度神经网络（DNN），用于麦克风阵列声源的定量表征。需要找到一种替代已经建立的基于模型的方法，它以相对较少的计算工作量提供精确的结果。该项目的工作旨在显著增加在这些和类似声学测量任务中适当使用深度神经网络的知识。首先，需要开发一种能够以可重复的方式生成大量合成测量数据的方法。在此基础上，预计将开发一种方法，在没有给定网格的情况下估计声源的位置和强度。此外，还需要训练一个神经网络来解决声源表征的反问题。最后，不是对单个源的贡献进行离散的、按频率的描述，而是试图找到一种方法来估计功率谱参数描述所需的数据。所有已开发的方法也应根据实验获得的数据进行评价。

Microphone array methods are an established approach for the localization and characterization of acoustic sources. Using suitable signal processing, the contributions of different sound sources can be mapped spatially. However, a low dynamic range makes it often difficult to assign the mapped source contributions to their causes and to identify the source mechanisms. For this reason, a number of different methods have been developed, which differ greatly in terms of accuracy, computational effort and robustness against interference. Depending on the measurement task, satisfactory results are not always achieved. Consequently, the development of improved methods is desirable.The main goal of the project is to establish deep neural networks (DNN) for the quantitative characterization of sound sources with microphone arrays. An alternative to already established model-based methods is to be found, which provides precise results with comparatively little computational effort. The work in the project aims at a significant increase in knowledge on the appropriate use of DNN for these and similar acoustic measurement tasks. First, a method is to be developed that can generate large quantities of synthetic measurement data in a reproducible manner. Based on this, the development of a method is foreseen that estimates the location and strength of sound sources without a given grid. In addition, a neural network is to be trained for the solution of an inverse problem for the characterization of sound sources. Finally, instead of the discrete, frequency-wise description of the contribution of individual sources, it is intended to find a method which estimates the data necessary for a parametric description of the power spectrum. All developed methods should also be evaluated on the basis of experimentally obtained data.