Deep Learning Approaches for Microphone Arrays in Acoustic Testing

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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.