# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
###### Abstract  
The high-speed train (HST) is one of the most important transport tools in China, and the sound quality of its interior noise affects passengers' comfort. This paper proposes a HST sound quality model. The model combines Mel-scale frequency cepstral coefficients (MFCCs), the most popular spectral-based input parameter in deep learning models, with convolutional neural networks (CNNs) to evaluate the sound quality of HSTs. Meanwhile, two input channels are applied to simulate binaural hearing so that the different sound signals can be processed separately. The binaural MFCC-CNN model achieves an accuracy of 96.2% and outperforms the traditional shallow neural network model because it considers the time-varying characteristics of noise. The MFCC features are capable of capturing the characteristics of noise and improving the accuracy of sound quality evaluations. Besides, the results suggest that the time and level differences in sound signals are important factors affecting sound quality at low annoyance levels. The proposed model is expected to optimize the comfort of the interior acoustic environment of HSTs. high-speed train; interior noise; sound quality; MFCC; CNN +
Footnote †: journal: Medical Image and Vision  
car by paired comparison methods. The evaluation results obtained by this method are accurate, but organizing the experiment is time-consuming. In order to optimize sound quality evaluation models, some researchers use linear regression methods to combine subjective test results with psychoacoustic parameters. [PERSON] et al. [3] established a quantitative linear regression model to study the relationship between Dichen sensation and tonality. [PERSON] et al. [11] investigated the frequency dependence of the annoyance of railway noise by using the multiple regression method. The model could well explain the empirical results. Considering the complexity and non-linearity of human hearing and noise characteristics, various intelligent methods have been introduced to assess sound quality. [PERSON] et al. [12] trained a neural network to predict the human perception of interior noise in HSTs. In automobile engineering, [PERSON] et al. [13] adopted a genetic algorithm to optimize a support vector regression (SVR) sound quality evaluation model. [PERSON] et al. [14] proposed a hybrid approach that combined the finite element method and an artificial neural network (ANN) to predict the low-frequency acoustic behavior of an auditory system. To evaluate diesel-engine-radiated noise quality under different operating conditions, a genetic algorithm combined with a support vector machine (SVM) model was proposed by [PERSON] et al. [15]. In recent years, researchers in different fields used deep learning models to study sound quality [16; 17]. The convolutional neural network (CNN) is one of the most widely used deep neural networks and is very effective at extracting features using convolutional kernels. [PERSON] et al. [18] constructed a CNN model to evaluate noise quality from internal combustion engines. [PERSON] et al. [19] developed a regularized deep CNN model to evaluate vehicle interior sound quality. The input data of CNN are an important parameter that affects the performance and effectiveness of the sound quality model. Generally, the neural network can have all the original information from the time series data of the collected sound samples. However, the amount of time domain data is relatively large. The data can be reduced if the frequency information of sound samples is selected as input. Yet, the frequency cannot show the time-varying characteristics of the sound samples. A spectrogram is an ideal input parameter that represents the frequencies of a signal as it varies with time. It contains both time and frequency domain information. Humans perceive frequencies on a nonlinear scale. It is easier for people to detect differences at lower frequencies than at higher frequencies.