@article {QIAN:2024:0736-2935:2020,
title = "Prediction and analysis of interior sound quality of high-speed trains based on XGBoost",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2024",
volume = "270",
number = "9",
publication date ="2024-10-04T00:00:00",
pages = "2020-2028",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2024/00000270/00000009/art00004",
doi = "doi:10.3397/IN_2024_3123",
author = "QIAN, KUN and SHEN, ZHENGHUA and TAN, JING and WANG, YANFU and LIU, KE and XIKANG, Du and JIYING, Duan",
abstract = "This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective
evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing
AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.",
}