# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 4 Experimental
### Experimental Results and Analysis
#### 4.3.2 Experimental Results of Event Classification on ESC-50  
In this paper, experimental validation is carried out on small sample data to illustrate the continued strength of the MAST model in the face of data limitations. The small scale data are the ESC-50 dataset. The data were divided into 5 folds, of which 4 folds were selected as the training set and 1 fold was selected as the test set for a total of 5 training sessions. The experiment was repeated 3 times using random seeds to obtain the average accuracy. The dataset was evaluated using the Accuracy. During the experimental data processing, the audio samples from ESC-50 were resampled and the sampling rate was set to 32 kHz, with the same specific experimental parameters as the AudioSet dataset.  
To verify that the proposed method has good classification accuracy even when the models are data-limited, various audio classification models were trained without pre-training the models on the AudioSet dataset. To ensure fairness, all algorithms in the experiments used the same training parameters and training samples, and the experimental results are presented in Table 5.  
As can be seen from Table 5, the recognition accuracy of MAST in this paper is higher than other algorithms when MAST is trained directly on the ESC-50 dataset, while the number of model parameters of the method is reduced relative to other algorithms. Since PANN and EfficientNet are convolutional neural network-based methods, the model is limited to achieving higher accuracy because the convolutional computation is often limited to the convolutional window size. AST, SepTr and our MAST are the recently emerged Vision Transformer-based models that tend to achieve the accuracy of MAST and SepTr 92.0 and 91.1, respectively. The reason for the lower accuracy of AST is that it is a more complex model and the fit is often not as good as it could be given the data constraints.  
In this paper, the proposed algorithm is pre-trained on AudioSet first, and the prediction accuracy is further improved in the small-scale ESC dataset by transfer learning. The experimental comparison results based on transfer learning are shown in the lower part of Table 5, where the pre-trained MAST model obtained an accuracy rate of 96.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Year** & **Params.** & **Accuracy** \\ \hline PANN [52] & 2020 & 81M & 90.5 \\ EfficientNet [56] & 2020 & 66M & 89.5 \\ AST [25] & 2021 & 87M & 88.7 \\ SepTr [47] & 2022 & 9.4M & 91.1 \\ MAST & 2022 & 36M & 92.0 \\ \hline AST [25] (pretrain) & 2021 & 87M & 95.6 \\ MAST (pretrain) & 2022 & 36M & 96.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the results of the algorithms based on the ESC-50 dataset.  
Figure 10 shows the confusion matrix generated by the pre-trained transfer method of this paper on the ESC-50 dataset. As can be observed, the best method in this paper accomplishes a high-accuracy classification of environmental sounds in ESC-50. The sound categories in this dataset contain 50 classes, using a numerical label form to represent specific label categories inside the dataset.  
As can be seen from Figure 10, the classification accuracy of the MAST (pretrain) method for all environment audio was above 87.5% and reached 100% classification accuracy for multiple category labels such as No. 1, 17, 20, 39, and 40. However, due to the similarity of sound between some of the categories, it is easy to confuse the process of sound recognition. A typical misclassified sample such as the 47 th sample label category has five audio samples incorrectly identified as the label category of No. 40, which correspond to the real labels of aircraft sound and helicopter sound, respectively, and their similarity and easy-to-confuse characteristics cause the model to misidentify.