Interior sound quality evaluation of high- speed trains -a literature review

Kun Qian, Zhenghua Shen, Jing Tan, Ke Liu, Yanfu Wang, Haoyang Li and Jian Zhao

International Journal of Rail Transportation, 2025, vol. 13, issue 2, 287-312

Abstract: The rapid advancement of the high-speed rail industry has led to an increase in passenger traffic on high-speed trains. As a result, there is an increasing concern about noise issues within the carriages. The evaluation of sound quality in high-speed trains has garnered significant research attention in recent years. The interior sound quality of high-speed trains can be evaluated through human subjective tests or AI-based models, with the latter gaining preference due to their efficiency, reduced labour intensity, and enhanced repeatability. Using AI-based models to objectively evaluate the interior sound quality in high-speed trains, circumvents the need for costly and labour-intensive subjective tests. This paper focuses on the subjective and objective evaluation methods of sound quality in high-speed trains, and the characteristics of sound quality in high-speed trains. Furthermore, this paper discusses the existing quantitative model for evaluating the sound quality of high-speed trains. This paper delves into the merits and demerits of various evaluation methods and models, and endeavours to explore future developments in evaluating interior sound quality in high-speed trains. This paper endeavours to comprehensively analyse current evaluation techniques and models while identifying the gaps and limitations in existing methodologies. Additionally, this paper underscores the necessity for the development of innovative approaches to evaluate sound quality in high-speed trains and proposes potential directions for future research. Overall, this study aspires to enrich the ongoing discourse on sound quality evaluation in high-speed trains and to provide insights that may guide the creation of more effective and reliable evaluation methods.

Date: 2025
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DOI: 10.1080/23248378.2024.2336504

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