# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 4 Experimental Results
### Experimental Results
Compared with the results on the validation data \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Validation Data**} \\ \hline  & \multicolumn{3}{c}{**Baseline Transformer**} & \multicolumn{3}{c}{**Multi-Scale Transformer**} \\ \hline  & & **F-Score (\%)** & **ER** & **F-Score (\%)** & **ER** \\ \hline  & NEB = 1 & 25.36 & 2.37 & 27.30 & 2.44 \\ \cline{2-6}  & NEB = 2 & 28.77 & 2.31 & 28.20 & 2.33 \\ \cline{2-6} w/o & NEB = 3 & 25.41 & 2.33 & 28.08 & 2.30 \\ \cline{2-6} Post & Average & **26.51** & **2.34** & **27.79** & **2.35** \\ \cline{2-6}  & Relative & - & - & **4.8\%** & **0\%** \\ \cline{2-6}  & Improvement & - & - & **4.8\%** & **0\%** \\ \hline  & NEB = 1 & 35.44 & 1.32 & 37.12 & 1.26 \\ \cline{2-6} w/ & NEB = 2 & 38.14 & 1.30 & 37.91 & 1.27 \\ \cline{2-6} Post & NEB = 3 & 35.47 & 1.33 & 37.64 & 1.26 \\ \cline{2-6} Processing & Average & **36.35** & **1.32** & **37.50** & **1.27** \\ \cline{2-6}  & Relative & - & - & **3.2\%** & **6.2\%** \\ \cline{2-6}  & Improvement & - & - & **3.2\%** & **6.2\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of event-based SED results on validation data between baseline Transformer model and multi-scale Transformer model. in Table 3, we observed the manifested performance improvement in ER on the evaluation data in Table 4.  
We also applied multi-scale features to the mean-teacher Transformer model, as shown in Figure 3. In Table 5, we compare its performance with that of the baseline mean-teacher model, without multi-scale features, on the validation data. As expected, we see a performance improvement by using multi-scale features on the mean-teacher model. Compared with the basic Transformer model in Table 3, the improvement obtained by using multi-scale features was more significant in the mean-teacher Transformer model in Table 5. The average F-score without post-processing shows a 10.1% relative improvement in the multi-scale mean-teacher Transformer model, while we only saw a 4.8% improvement in Table 3. When post-processing was used, the relative improvement reduced to 6%. This is still better than that of the basic Transformer model in Table 3. We consistently obtained better results using the multi-scale features in the mean-teacher model when the NEB changes from one to three. These results confirm that the multi-scale features are more effective in the mean-teacher Transformer model than in the basic Transformer model. Since the mean-teacher-based neural network models are now preferred over basic models, the results seem to be more inspiring for the performance improvement in SED. Table 6 shows the test results of the mean-teacher Transformer model using multi-scale features on the evaluation data, and we observed an 11.6% relative improvement in the average F-score compared with the baseline Transformer model. As shown in Tables 3 and 4, we observed better performance improvement on the evaluation data than the validation data. For the ER, we saw consistent improvements in validation data as well as evaluation data. In Table 7, we compared the results of our work with those of the baseline system of DCASE 2019 based on CRNN [1] and those in [25] where they applied a Transformer model for SED. Since the two systems were developed using the dataset of DCASE 2019 as in our work, the comparison seems to be reasonable.