Sound quality evaluation of high-speed train interior noise by adaptive Moore loudness algorithm

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.

中文概要

目的

高速列车的车内噪声以中低频为主,传统的线性和A 计权声压级都无法客观描述人耳的听觉感受。本文旨在探索Moore 响度应用于车内声品质分析的可行性。

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。