# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 4 Experimental
### Experimental Results and Analysis  
#### 4.3.1 Experimental Results of Event Classification on AudioSet  
This paper begins with experimental validation on large-scale data to illustrate the advantages of the MAST model's ability to model with fully fitted parameters. As AudioSet has 2 million data samples, which matches the data sample size required for the experiments, the specific data pipeline was used in the same way as in [25]. We trained the model using the Adam optimizer with an initial learning rate of \(1\times 10^{-4}\). A total of 30 epochs were experienced by setting the learning decay coefficients to [0.1, 0.05, 0.02], with the first 3 epochs used for warm-up training. Finally, mAP was used as our main evaluation metric. For the extraction of audio features, the frame length of each frame was set to 1024, the frameshift to 320, the number of filter banks to 64, the sampling rate 32 kHz, the audio length was unified to 10 s and all feature vectors were normalized to the 0 to 1 interval. The results of the mAP curves of the proposed network model and the AST model are compared in Figure 9. It can be seen that the average accuracy of the algorithm has improved significantly compared with the AST algorithm. When the algorithm in this paper was iterated to the 23 rd epoch, the mAP increased to about 0.457, and finally in a steady rise to about 0.461; when the AST algorithm was iterated to the 5 th epoch, the mAP only increased to about 0.458, the model overfitting phenomenon occurred in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title 1** & **Training Set** & **Validation Set** & **Total** \\ \hline Silent scene & 1057 & 264 & 1321 \\ Teachers teaching scene & 1367 & 342 & 1709 \\ Interaction scene & 1899 & 475 & 2374 \\ Total & 4323 & 1081 & 5404 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The specific sample number distribution of the CSID dataset. subsequent training process and the model mAP accuracy kept oscillating and decreased to some extent. To verify that the proposed method is advanced in the field of audio classification, the algorithms in this paper are compared with the mainstream audio classification algorithms, and to ensure fairness, all models are trained in the full_train method on Audi-oSets data and tested for comparison. Table 3 shows the results of comparing our model with other representative state-of-the-art methods. The previous best accuracy on the AudioSet dataset is 0.459, while our proposed MAST can achieve 0.461, which is an improvement of 0.002 compared with the previous best result, but the number of model parameters in MAST is significantly lower compared with AST. Compared with other CNN-attention hybrid models, the algorithm in this paper may be higher in terms of the number of model parameters, because the method is based on the transformer architecture, which also enables a significant improvement in detection accuracy and achieves a balance between detection accuracy and model complexity. To show the effectiveness of the multi-channel features and the Time-Frequency Domain Local Enrichment Module proposed in this paper, ablation experiments were conducted on a large-scale AudioSet dataset. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Multi-Channel Features** & **TLEM** & **FLEM** & **mAP** \\ \hline Multi-scale Transformer & \(\times\) & \(\times\) & \(\times\) & 0.451 \\ +Multi-channel features & \(\surd\) & \(\times\) & \(\times\) & 0.454 \\ +TLEM & \(\surd\) & \(\surd\) & \(\times\) & 0.457 \\ +FLEM & \(\surd\) & \(\surd\) & \(\surd\) & 0.461 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ablation experiments of the algorithms in this paper.