基于多尺度梅尔倒谱系数的转辙机声信号状态识别方法

为了准确地识别铁路转辙机所处的工作状态，保证列车能够安全行驶并转向，提出了一种基于声音信号的转辙机状态识别方法。首先将声音信号预处理后提取其梅尔倒谱系数（MFCC）；为更加全面表征转辙机声信号的特点，对MFCC进行改进得到多尺度MFCC特征；引入卷积神经网络（CNN）构建转辙机声信号识别模型，并采用五折交叉验证法获得两种特征的识别准确率。本实验将S700K型转辙机在四种状态下运行时采集的真实声音信号进行训练和测试。实验结果表明，多尺度MFCC特征可使转辙机声音状态识别准确率至少提高7.5%。并且在低信噪比下，多尺度MFCC特征也有更好的表现，其准确率相较传统MFCC可提升35%。

State Recognition Method of Switch Machine Acoustic Signal Based on Multiple-scale Mel Frequency Cepstrum Coefficients

Accurately identifying the working state of railway switch machines can prevent the failures from affecting the efficiency and safety of railway transportation. We proposed a state recognition method to estimate the working state for switch machines based on acoustic analysis by improving the MFCC method and obtained multi-scale MFCC (Ms-MFCC) features, which provided a more comprehensive understanding of the switch-machine acoustic signals. Convolutional neural network (CNN) was introduced to construct the acoustic signal recognition model, and the five-fold cross validation method was employed to obtain the recognition accuracy. In this study, the real acoustic signals collected by the S700K switch machine in four states were trained and tested. Our results show that the Ms-MFCC feature can improve the accuracy of the switch machine''s acoustic state recognition by at least 7.5%. And in low SNR cases, the accuracy can be improved by 35%.