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: 5268
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, 2017, 18(9): 690-703.
@article{title="Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm",
author="Le Luo, Xu Zheng, Zhi-yong Hao, Wen-qiang Dai, Wen-ying Yang",
journal="Journal of Zhejiang University Science A",
volume="18",
number="9",
pages="690-703",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1600287"
}
%0 Journal Article
%T Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm
%A Le Luo
%A Xu Zheng
%A Zhi-yong Hao
%A Wen-qiang Dai
%A Wen-ying Yang
%J Journal of Zhejiang University SCIENCE A
%V 18
%N 9
%P 690-703
%@ 1673-565X
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1600287
TY - JOUR
T1 - Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm
A1 - Le Luo
A1 - Xu Zheng
A1 - Zhi-yong Hao
A1 - Wen-qiang Dai
A1 - Wen-ying Yang
J0 - Journal of Zhejiang University Science A
VL - 18
IS - 9
SP - 690
EP - 703
%@ 1673-565X
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1600287
Abstract: An online experiment to acquire the interior noise of a China Railways High-speed (CRH) train showed that it was mainly composed of middle-low frequency components and could not be described properly by linear or A-weighted sound pressure level (SPL). Thus, the appropriate way to evaluate the high-speed train interior noise is to use sound quality parameters, and the most important is loudness. To overcome the disadvantages of the existing loudness algorithms, a novel signal-adaptive Moore loudness algorithm (AMLA) based on the equivalent rectangular bandwidth (ERB) spectrum was introduced. The validation reveals that AMLA can obtain higher accuracy and efficiency, and the simulated dark red noise conforms best to the high-speed train interior noise by loudness and auditory assessment. The main loudness component of the interior noise is below 27.6 ERB rate (erbr), and the sound quality of the interior noise is relatively stable between 300–350 km/h. The specific loudness components among 12–15 erbr stay invariable throughout the acceleration or deceleration process while components among 20–27 erbr are evidently speed related. The unusual random noise is effectively identified, which indicates that AMLA is an appropriate method for sound quality assessment of the high-speed train under both steady and transient conditions.
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.
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