Microphone arrays have long been used to characterize and locate sound sources. However, existing algorithms for processing the signals are computationally expensive and, consequently, different methods need to be explored. Recently, the trained iterative soft thresholding algorithm (TISTA), a data-driven solver for inverse problems, was shown to improve on existing approaches. Here, a more in-depth analysis of its robustness and frequency dependence is provided using synthesized as well as real measurement data. It is demonstrated that TISTA yields favorable results in comparison to a covariance matrix fitting inverse method, especially for large numbers of sources.
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