# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
###### Abstract
Mel-scale frequency cepstral coefficients (MFCCs) are one of the most normally used feature extraction techniques in speech recognition [20]. It converts the frequencies in the spectrogram to the Mel scale. The Mel scale simulates the human auditory system by being more discriminative at lower frequencies and less discriminative at higher frequencies. The HST noise is concentrated at low frequencies [6]. Using MFCCs as the input data can reflect the sound characteristics of HSTs. The above-mentioned research shows that there are still some problems that need to be addressed:  
1. The most commonly used evaluation method is the A-W SPL, and psychoacoustic parameters are also used, but these parameters consider the time domain or frequency domain information individually. They cannot completely reflect the human hearing perception. 2. Some studies use linear regression, traditional shallow neural networks, and other techniques to optimize the subjective evaluation, but the time and frequency information of noises have not been considered simultaneously. 3. There are many sound quality models for automotive engineering, but using these models to evaluate the sound quality of HSTs may not be effective. The reason is that the HST travels faster, and its noise is higher and more concentrated below 100 Hz [6]. Meanwhile, automobile noise is more predominant between 100 and 600 Hz [21]. In most previous studies, the sound samples were collected by microphones, so the influence of binaural hearing could not be considered in the previous models. Therefore, this paper aims to develop a new sound quality model for HSTs to overcome the above challenges. Figure 1 shows the basic structure of the proposed model. The proposed model was inspired by cognitive science which attempts to combine perspectives of biology, neuroscience, and psychology to gain a better understanding of human cognitive faculties. It tries to simulate the steps of the hearing process, and it contains Mel-scale filter banks, MFCC features, and CNNs. The main idea is to let the model function like the hearing system. The non-uniform bandpass filter banks are designed to imitate the frequency resolution of human hearing. The MFCC is used to replace the sound features after filter banks. A CNN's architecture is analogous to the connectivity pattern of the human brain, so the CNN is used to process sound stimuli. The model has a more similar architecture to the biological hearing system than the models in previous studies. It better represents the nonlinearity of human hearing and makes the model highly accurate. The proposed model has the following features:  
1. The human auditory system works like a set of filters, so the model uses Mel-scale filter banks to simulate the system. The filter banks separate the input sound signal into multiple components and attenuate the components differently. Compared to the A-W SPL, the Mel-scale filter banks provide a better resolution at low frequencies and less resolution at high frequencies, which mimics the nonlinear human perception of sound. 2. The model converts noise data into MFCC features, so both time domain and frequency domain information can be used simultaneously as input parameters for the CNN. At the same time, MFCCs help find out the key information in the noise sample, especially the low-frequency characteristics, thus improving the model accuracy. 3. The CNN with multiple hidden layers is used to simulate the brain's processing of sound signals. It is considered to be more powerful than traditional neural networks because of its high accuracy in speech recognition problems. Each layer of the CNN can be used to extract sound features. 4. Two input channels are applied to simulate binaural hearing so that the different sound signals can be processed separately. The time and level differences of sound signals are important factors affecting sound quality. The rest of this paper is organized as follows. The next section describes the collection of HST interior noise samples and how the subjective test is conducted.