*Article* **Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure**

#### **Yifeng Wang 1, Ping Wang 1, Qihang Wang 1, Zhengxing Chen 1 and Qing He 1,2,3,\***



Received: 5 January 2020; Accepted: 15 February 2020; Published: 18 February 2020

**Abstract:** This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the e ffect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segmen<sup>t</sup> the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the e ffects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the e ffects of rail grinding.

**Keywords:** urban rail transit interior noise; smartphone sensing; XGBoost classifier; railway maintenance
