School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, People's Republic of China.
Auto Engineering Research Institute, BYD Auto Industry Co., LTD, Shenzhen 518118, People's Republic of China.
J Acoust Soc Am. 2024 Aug 1;156(2):989-1003. doi: 10.1121/10.0028130.
In order to improve the prediction accuracy of the sound quality of vehicle interior noise, a novel sound quality prediction model was proposed based on the physiological response predicted metrics, i.e., loudness, sharpness, and roughness. First, a human-ear sound transmission model was constructed by combining the outer and middle ear finite element model with the cochlear transmission line model. This model converted external input noise into cochlear basilar membrane response. Second, the physiological perception models of loudness, sharpness, and roughness were constructed by transforming the basilar membrane response into sound perception related to neuronal firing. Finally, taking the calculated loudness, sharpness, and roughness of the physiological model and the subjective evaluation values of vehicle interior noise as the parameters, a sound quality prediction model was constructed by TabNet model. The results demonstrate that the loudness, sharpness, and roughness computed by the human-ear physiological model exhibit a stronger correlation with the subjective evaluation of sound quality annoyance compared to traditional psychoacoustic parameters. Furthermore, the average error percentage of sound quality prediction based on the physiological model is only 3.81%, which is lower than that based on traditional psychoacoustic parameters.
为了提高车内噪声音质预测的准确性，提出了一种基于生理响应预测指标（即响度、尖锐度和粗糙度）的新型音质预测模型。首先，通过将外耳和中耳有限元模型与耳蜗传输线模型相结合，构建了人耳声音传输模型。该模型将外部输入噪声转换为耳蜗基底膜响应。其次，通过将基底膜响应转换为与神经元放电相关的声音感知，构建了响度、尖锐度和粗糙度的生理感知模型。最后，以计算出的生理模型的响度、尖锐度和粗糙度以及车内噪声的主观评价值作为参数，通过 TabNet 模型构建了一个音质预测模型。结果表明，与传统心理声学参数相比，人耳生理模型计算出的响度、尖锐度和粗糙度与声音质量烦恼的主观评价具有更强的相关性。此外，基于生理模型的音质预测的平均误差百分比仅为 3.81%，低于基于传统心理声学参数的预测。
