CLC number: TB532
On-line Access: 2017-09-04
Received: 2016-04-03
Revision Accepted: 2016-10-08
Crosschecked: 2017-08-15
Cited: 0
Clicked: 4948
Citations: Bibtex RefMan EndNote GB/T7714
Le Luo, Xu Zheng, Zhi-yong Hao, Wen-qiang Dai, Wen-ying Yang. Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A1600287 @article{title="Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm", %0 Journal Article TY - JOUR
Abstract: This is a generally well written paper describing sound quality evaluation of high-speed train interior noise by AMLA method. It is original and the paper makes a useful contribution to a challenging topic.
基于自适应Moore响度算法研究高速列车车内声品质创新点:1. 提出了一种自适应Moore响度算法(AMLA),该算法可有效提升计算的精度和效率;2. 采用AMLA分析了高速列车车内噪声在不同工况下的声品质特征。 方法:1. 基于信号的等矩形带宽(ERB)谱,提出AMLA方法的理论;2. 参照ANSI标准中的仿真信号,评价AMLA的计算精度和效率;3. 采用AMLA辨别有色噪声信号与车内噪声样本,验证声品质分析的有效性;4. 结合在线搭载试验,运用AMLA分析稳态工况(不同行车速度和空间位置等)和瞬态工况(加速和减速等)下的车内声品质特征。 结论:1. 相比传统方法,AMLA方法的计算精度和效率相对较高,且适用范围更广;2. 高速列车的车内噪声与深红噪声信号具有相似的特征响度分布;3. 稳态工况下,车内噪声的响度成分集中在27.6 erbr以内,在300~350 km/h的速度区间内车内声品质较稳定,空间分布特征为"端部大、中间小";4. 瞬态工况下,车内噪声在20~27 erbr内的响度成分与列车速度密切相关,而在12~15 erbr内的成分相对稳定。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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