Correlated beamforming based on deconvolution methods for identified vehicle exterior wind noise sources and interior noise

Sound source identification based on beamforming is widely used today as a spatial sound field visualization technology in wind tunnel experiments for vehicle development. However, the conventional beamforming technique has its inherent limitations. To improve the performance, three deconvolution methods CLEAN, CLEAN-SC and DAMAS were investigated. These algorithms were applied for the wind noise sources identification on a production car. After analysis of vehicle exterior wind noise sources distribution, to study the energetic contribution of identified exterior noise sources to interior noise, correlation analysis between them were conducted. The results show that, the algorithm CLEAN-SC based on spatial source coherence shows the outstanding capability to remove the sidelobes for the uncorrelated wind noise sources, while CLEAN and DAMAS which based on point spread function have definite limitations. In the application aspect, the main noise source outside vehicle is from the wheelhouse region, then follows the rearview mirror with much lower sound energy. However, noise from the mirror contributes most to the vehicle interior, while the contribution from wheelhouse region ranks the second place. In addition, windshield wipers and door handle can do perceptible contributions to vehicle interior noise at some characteristic frequency bands.