Objective evaluation of interior noise booming in a passenger car based on sound metrics and artificial neural networks
Booming sound is one of the important sounds in a passenger car. The aim of the paper is to develop the objective evaluation method of interior booming sound. The development method is based on the sound metrics and ANN (artificial neural network). The developed method is called the booming index. Previous work maintained that booming sound quality is related to loudness and sharpness  the sound metrics used in psychoacoustics  and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20Hz and 20kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200Hz. The relationship between the booming index and sound metric is identified by an ANN. The updated booming index has been successfully applied to the objective evaluation of the booming sound quality of mass-produced passenger cars.
booming car sound metrics neural network acceleration subjective evaluation