Localization and quantification of noise sources are important to fulfill customer and regulation requirements in a such competitive sector like automotive manufacturing. Wind tunnel testing and acoustic mapping techniques based on microphone arrays can provide accurate information on these aspects. However, it is not straightforward to get source positions and strengths in these testing conditions. In fact, the car is a 3D object that radiates noise from different parts simultaneously, involving different noise generation mechanisms such as tire noise and aerodynamic noise. Commonly, acoustic maps are produced on a 3D surface that envelopes the objects. However, this practice produces misleading and/or incomplete results, as acoustic sources can be generated outside the surface. When the hypothesis of sources on the model surface is removed, additional issues arise. In this paper, we propose exploiting an inverse method tailored to a volumetric approach. The aim of this paper is to investigate the issues to face when the method is applied to automotive wind tunnel testing. Two different kinds of problem must be considered: On the one hand, the results of inverse methods are strongly influenced by the problem definition, while, on the other hand, experimental conditions must be taken into account to get accurate results. These aspects have been studied making use of simulated experiments. Such a controlled simulation environment, by contrast to a purely experimental case, enables accurate assessment of both the localization and quantification performance of the proposed method. Finally, a set of scores is defined to evaluate the resulting maps with objective metrics.

Interior sound measurements play an important role in vehicle development and refinement. Sometimes hundreds of microphones are installed in an automotive cabin. During test preparation and execution, a lot of time is spent in determining the microphone positions and in tracking cables to the data acquisition channel. A smart acoustic localization approach is presented to automate this process and to realize considerable time gains. It is based on estimating the distance between a microphone and (at least 4) sources by acoustic time-of-arrival measurements, combined with novel algorithms that cope with reflections and non-line-of-sight issues. The method will be illustrated using in-vehicle measurements.