Acoustic source localization techniques in combination with microphone array measurements have become an important tool for noise reduction tasks. A common technique for this purpose is acoustic beamforming, which can be used to determine the source locations and source distribution. Advantages are that common algorithms such as conventional beamforming, functional beamforming or deconvolution techniques (e.g., Clean-SC) are robust and fast. In most cases, however, a simple source model is applied and the Green’s function for free radiation is used as transfer function between source and microphone. Additionally, without any further signal processing, only stationary sound sources are covered. To overcome the limitation of stationary sound sources, two approaches of beamforming for rotating sound sources are presented, e.g., in an axial fan.Regarding the restrictions concerning source model and boundary conditions, an inverse method is proposed in which the wave equation in the frequency domain (Helmholtz equation) is solved with the corresponding boundary conditions using the finite element method. The inverse scheme is based on minimizing a Tikhonov functional matching measured microphone signals with simulated ones. This method identifies the amplitude and phase information of the acoustic sources so that the prevailing sound field can be with a high degree of accuracy.

Tire/road noise is a highly relevant topic for improving the comfort and experience of drivers and residents living in high-traffic areas. With the growing numbers of electric cars, the relevance of tire noise even increases since it is the dominant sound source in the middle-speed range. We investigate acoustic sources relevant to tire/road interactions. In doing so, we apply different sound source localization algorithms to measurement data acquired inside a large measurement trailer equipped with microphone arrays. The methods for sound source identification used are well-known beamforming-based algorithms and inverse schemes based on finite element or boundary element simulations. The latter schemes require the identification of the acoustic properties of the trailer in the stationary case. In this contribution, we present the characterization process and the results of the sound source localization in this stationary case.

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