Acoustics and Noise Signal Processing Laboratory, Department of Mechanical Engineering, Inha University, 253 Yonghyun Dong, Inchon 402-751, Republic of Korea.
Appl Ergon. 2009 Sep;40(5):860-9. doi: 10.1016/j.apergo.2008.11.006. Epub 2008 Dec 17.
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 20 Hz and 20 kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200 Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200 Hz. 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.
轰鸣声是乘用车的重要声音之一。本文旨在开发车内轰鸣声的客观评价方法。该方法基于声音指标和人工神经网络（ANN）。所开发的方法称为轰鸣声指标。之前的研究认为，轰鸣声质量与响度和尖锐度有关——这是心理声学中使用的声音指标——并且通过使用整个频率范围内（20 Hz 至 20 kHz）信号的响度和尖锐度来开发轰鸣声指标。在本文中，发现轰鸣声质量与 200 Hz 以下频率的响度有效相关；因此，通过使用低于 200 Hz 频率的低通滤波器对信号进行滤波来更新轰鸣声指标。通过 ANN 确定了轰鸣声指标与声音指标之间的关系。更新后的轰鸣声指标已成功应用于批量生产乘用车的轰鸣声质量的客观评价。
