Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)
Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)
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DOI：
10.1088/0957-0233/27/1/015801
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发表时间：
2015
影响因子：
2.4
通讯作者：
E. Zhang;L. Hou;Chao Shen;Yingliang Shi;Yaxiang Zhang
中科院分区：
文献类型：
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作者：
E. Zhang;L. Hou;Chao Shen;Yingliang Shi;Yaxiang Zhang
为了更好地解决主观声品质评价结果与客观心理声学参数之间的复杂非线性问题，提出了一种基于粒子群优化算法（PSO）的反向传播神经网络（BPNN）声品质预测方法。为了验证该方法的有效性和准确性，以B级车从怠速到120 km h−1的噪声信号为目标，通过人工头测量。此外，本文还采用等级评价法对车内声品质烦恼度进行了主观评价实验，得到了各样本的烦恼度。利用Artemis软件计算了各噪声样本的主要客观心理声学参数。这些参数包括响度、锐度、粗糙度、波动、音调、清晰度指数（AI）和A加权声压级。在此基础上，建立了三种具有相同神经网络结构的评价模型：标准BP神经网络模型、遗传算法-反向传播神经网络（GA-BPNN）模型和PSO-反向传播神经网络（PSO-BPNN）模型。通过对三种模型的网络进行训练和基于实验数据的评价预测，证明了PSO-BPNN方法能够更快地实现收敛，提高了声品质的预测精度，为进一步控制车内声品质奠定了基础。
To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h−1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles.