Experimental Analysis and Design Guidelines for Microphone Virtualization in Automotive Scenarios

In this article, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs.