基于径向基函数神经网络的车内噪声品质评价系统

为了高效而准确地对车内噪声品质进行评价,针对B级车稳态工况下的车内噪声,建立了基于径向基函数(RBF)神经网络的声品质评价系统。用等级评分法对30个稳态噪声信号进行了主观评价试验,并通过相关分析得出了对声品质有重要影响的客观参量。采用RBF神经网络构建了车内噪声品质的评价模型,其预测平均相对误差为4.5%。以评价模型为基础,采用模块化设计方法和多线程并行处理技术,设计了基于虚拟仪器的声品质评价系统。测试结果表明:该系统比传统的主观评价试验系统的测试时间缩短了90%,并提高了评价结果的质量。

Vehicle interior noise sound quality evaluation system based on RBF neural network

An evaluation system was established to evaluate efficiently and accurately the sound quality of the interior noise of B-class vehicle in the steady working conditions based on the radial basis function(RBF) neural network.The subjective evaluation tests were carried out for 30 steady noise signal samples with the grade estimation method.The objective parameters having significant effect on the sound quality were extracted by the correlation analysis.An evaluation model was built to predict the sound quality of the vehicle interior noise using an RBF neural network,its average relative predict error is 4.5%.Based on the predict model,using the modular design concept and the multi-threaded parallel processing technique,a virtual instrument based sound quality evaluation system was designed.The field test results showed that compared with traditional subjective evaluation method,the designed system reduces the test time by 90%,and the quality of the evaluation is better.