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Objective Evaluation of Interior Sound Quality in Passenger Cars Using Artificial Neural Networks

机译:客观评价人工神经网络乘用车内置音质的客观评价

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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.
机译:在这项研究中,测量了乘用车的内部噪音,并计算了包括声压级,响度,清晰度和粗糙度的音质度量。设计人工神经网络旨在成功应用汽车内部噪音以及众多不同的技术领域,旨在克服实验困难,节省成本,时间和劳动力。通过使用使用实验值设计的人工神经网络来估计声压级,响度,清晰度和粗糙度。比较预测值和实验结果。比较结果表明,实现人工智能模型是估计汽车内部噪音的音质的适当模型。可靠性值通过使用统计分析计算为0.9995。

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