Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

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.