# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 4 Results and Discussions
### Performance Comparison with Other Models
#### 4.3.2 The Influence of Binaural Hearing and MFCC  
Humans receive sound with two ears, but in many studies, the sound quality models were constructed with only one input channel. The single-input models did not consider the difference in sound between the two ears. Therefore, a monaural MFCC-CNN model was established to investigate further whether considering binaural hearing would help the model perform better in the sound quality evaluation. At the same time, a model that used time series data as input was also created to verify whether MFCC features helped to improve the evaluation accuracy. The structure of the two models is shown in Figure 19.  
The training progress of the two models is shown in Figure 20. The testing accuracy of the monaural MFCC-CNN model reached 91.7%, and the binaural time series CNN model had a lower testing accuracy (87.1%). The accuracy of both models was lower than the binaural MFCC-CNN model.  
Figure 17: Shallow neural network sound quality model.  
Figure 18: Evaluation performance of the shallow neural network model.  
Comparing the monaural and binaural models, it is clear that the evaluation is more accurate with extra input. On the one hand, an extra input channel provides more noise data; thus, more sound features are provided. On the other hand, the difference between the input data has a significant impact on the accuracy of the model, and such a difference is also an important factor for the subjective evaluation of sound quality. Meanwhile, although the time series model uses a binaural input, the accuracy (87.1%) of the model is even lower than that of the shallow neural network model. Generally, the model can have all the original information from the input time series data. However, the amount of time-domain data is relatively large and full of unnecessary information, and it is difficult for the model to find the key feature that affects the subjective evaluation. For this reason, the application of MFCC features not only helps reduce the input data dimensions but also provides frequency domain information that facilitates the distinction of various noise samples and improves the accuracy of the model.  
To investigate the prediction distribution further, the confusion matrix of testing accuracy of each model is shown in Figure 21. In Figure 21a, the highest accuracy (97.2%) appears at 'Moderately', while the lowest accuracy (86.7%) appears at 'Not at all'. Compared with the binaural MFCC-CNN model, the monaural MFCC-CNN model is less accurate in the assessment of 'Not at all' and 'Slightly'. Two 'Not at all' responses are wrongly evaluated as 'Moderately', and three 'Slightly' responses are wrongly evaluated as 'Moderately'. It appears that the monaural model is more willing to evaluate the sample as 'Moderately'. Perhaps the time and the level difference between the two ears are crucial factors affecting the subjective evaluation at a low annoyance level. However, the monaural MFCC-CNN model could not take such differences into account, which causes lower accuracy. As can be seen from Figure 21b, the binaural time series CNN model has the highest accuracy (93.3%) in 'Not at all' and the lowest accuracy (83.3%) in 'Very'. The accuracy decreases with the increase in annoyance level.  
Figure 19: Different sound quality prediction models for comparison: (**a**) Monaural MFCC-CNN model; (**b**) Binaural time series CNN model.  
Figure 21: Confusion matrix of (**a**) The monaural MFCC-CNN model; (**b**) The binaural time series CNN model.  
Figure 20: The training progress of different sound quality models: (**a**) Training accuracy and (**b**) Training loss of the monaural MFCC-CNN model; (**c**) Training accuracy and (**d**) Training loss of the binaural time series CNN model.