车内综合烦躁度评价的时序平滑激励级谱CNN模型

激励级时频谱可用于建立车辆声品质卷积神经网络(CNN)评价模型。但是相对于时变声特征的波动性,车内声品质的瞬时主观评价曲线具有平滑特性,利用波动的声特征序列建立时变声品质评价模型,导致预测曲线呈现波动性,所以时序波动激励级谱的直接使用也会制约车内噪声整体综合烦躁度CNN评价模型的性能。本文中,首先利用Savitzky-Golay滤波器对激励级谱进行时域平滑处理;然后使用CNN构建车内噪声的综合烦躁度全局主观评价结果与时序平滑激励级谱之间的映射关系,建立基于时序平滑激励级谱的整体综合烦躁度CNN评价模型;最后采用留一法进行交叉检验,结果表明相比于激励级谱CNN模型,时序平滑激励级谱CNN模型对车内噪声整体综合烦躁度的评价性能更好,提高了预测精度(误差均值降低10.43%)、稳定性(误差方差降低44.26%)与一致性(Pearson相关系数提高4.13%),说明相比于激励级谱,时序平滑谱对车内综合烦躁度的解析能力更强。

Annoyance Evaluation Model of Vehicle Interior Noise Based on Time-series Smoothed Excitation Level Spectrum CNN Model

Excitation level time-frequency spectrum can be used to establish the convolution neural network(CNN)model of vehicle sound quality evaluation(SQE).However,due to the discrepancy between the fluctuation characteristic of time-varying sound and the smooth characteristic of the instantaneous subjective evaluation curve of vehicle interior sound quality,the time-varying SQE model produces a fluctuating response to an input of fluctuating sound feature sequences.The performance of the CNN model of the overall annoyance evaluation of vehicle interior noise will be limited by directly using the fluctuating excitation level spectrum in time domain.In this paper,the Savitzky-Golay filter is used to smooth the excitation level spectrum in time domain,and CNN is used to build the mapping relationship between the overall subjective evaluation results of the comprehensive annoyance degree of vehicle interior noise and the time-series smoothed excitation level spectrum so that the overall annoyance CNN evaluation model based on the time-series smoothed excitation level spectrum is established.The leave-one-out cross-validation results indicate that compared with the excitation level spectrum CNN model,the time-series smoothed excitation level spectrum CNN model has better performance in overall annoyance evaluation of vehicle interior noise,with improvement in prediction accuracy(mean error decreased by 10.43%),stability(prediction variance decreased by 44.26%)and consistency(the Pearson correlation coefficient increased by 4.13%).The time-series smoothed excitation level spectrum can better represent the overall annoyance of vehicle interior noise than the excitation level spectrum.