| 贾尚帅,潘德阔,阮沛霖,郑旭.利用卷积网络的高速列车主观声品质预测*[J].,2022,41(4):646-653 |
| 利用卷积网络的高速列车主观声品质预测* |
| Prediction of Sound Quality of High-speed Train Using Convolutional Network |
| 投稿时间：2021-06-16 修订日期：2022-06-27 |
| 中文摘要: |
| 随着高速列车在中国的高速发展,乘客对舒适性的要求也在提高,因此高速列车内声学舒适性是一个需要研究和解决的问题。首先,本文基于声学人工头设备,获取了高速列车行驶在350 km/h速度下不同车厢不同区域的双耳噪声样本,并对其分别开展了主观声学评价和基于响度、尖锐度、粗糙度和抖动度等参数的客观声品质分析。结果表明,350 km/h速度下高速列车车内噪声能量集中在3000 Hz以内,风挡区域是舒适性评价较差的位置,而响度是影响主观评价最大的因素。其次,利用卷积神经网络算法将主观评价结果与高速列车噪声样本相关联,建立了车内噪声主观声品质预测模型,并与基于BP神经网络的预测模型进行了对比。结果表明,基于卷积神经网络的主观声品质预测模型具有更高的预测精度,可以用于指导高速列车车内声学舒适性的优化设计。 |
| 英文摘要: |
| With the rapid development of high-speed train (HST) in China, passengers" requirements for comfort are also increasing. Therefore, how to improve the interior noise and comfort of HST is a problem that needs to be studied and resolved. Firstly, with the artificial head device, this paper obtains binaural noise samples from different areas in different cabins of HST at a speed of 350 km/h. Then subjective evaluations and analysis with loudness, sharpness, roughness, and fluctuation strength are carried out. The results show that the interior noise energy of high-speed trains at 350 km/h is concentrated within 3000 Hz. The poorest evaluation result appears at windshield area, and the loudness is the most important factor affecting the subjective evaluation. Secondly, the convolutional neural network (CNN) is applied to build a sound quality prediction model between the subjective evaluation results and the HST noise samples, and the model is compared with the prediction model based on the BP neural network. The results show that the CNN prediction model has higher prediction accuracy and can guide the optimization design of the HST. |
| DOI：10.11684/j.issn.1000-310X.2022.04.017 |
| 中文关键词: 高速列车1 车内噪声2 声品质3 卷积神经网络4 |
| 英文关键词: high speed train interior noise sound quality convolutional neural network |
| 基金项目:国家自然科学基金项目（51705454） |
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