# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 5 Experimental and Performance Evaluation
### Results and Discussion
#### 5.2.3 S-CRNN-Based Classifier  
In this section, we use a multi model classifier that is, S-CRNN, which employs two parallel CRNNs for the two subclasses in the dataset. Subclass 1 includes brakes squeaking, car sounds, and large vehicle sounds, while subclass 2 consists of sound events in the classes of children, people speaking, and people walking as listed in Table 6.  
Figures 9 and 10 make it clear that S-CRNN outperforms the baseline model [1] in ER and F1-score across both the Dev-Set and Eval-Set. For example, for car events, the lowest ER of 0.33 is obtained using S-CRNN for the Dev-Set, whereas the baseline [1] achieves an F1-score of 74. The Dev-Set yields the lowest F1-score of 0 for children events and an ER of 1.36 in the baseline model.  
Figures 11 and 12 compare the overall ER and F1-score of S-CRNN with the baseline model and state-of-the art performer [2]. Clearly showing that using the S-CRNN model improved the overall F1-score by almost 10 % and the ER to 0.11 on the Eval-Set. In addition, the S-CRNN performed well on the Dev-Set, with an overall improvement of 2% in the F1-score is 0.3 in ER.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Dev-Set**} & \multicolumn{4}{c}{**Eval-Set**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Baseline [1]**} & \multicolumn{2}{c}{**S-CRNN**} & \multicolumn{2}{c}{**Baseline [1]**} & \multicolumn{2}{c}{**S-CRNN**} \\ \cline{2-9}
**Sound Events Class** & \multicolumn{2}{c}{**MFCC**} & \multicolumn{2}{c}{**MFCC+RASTA-PLP**} & \multicolumn{2}{c}{**MFCC**} & \multicolumn{2}{c}{**MFCC+RASTA-PLP**} \\ \cline{2-9}  & **ER** & **F1-Score** & **ER** & **F1-Score** & **ER** & **F1-Score** & **ER** & **F1-Score** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Brakes \\ Squeaking \\ \end{tabular} } & \multirow{3}{*}{0.98} & \multirow{3}{*}{4} & \multirow{3}{*}{0.69} & \multirow{3}{*}{48} & \multirow{3}{*}{0.92} & \multirow{3}{*}{16.5} & \multirow{3}{*}{0.69} & \multirow{3}{*}{55} \\ \cline{1-1} \cline{5-5} \cline{7-7}  & & & & & & & & \\ \hline Car & 0.57 & 74 & 0.33 & 67 & 0.76 & 61.5 & 0.14 & 66 \\ \hline Children & 1.35 & 0 & 0.87 & 17 & 2.6 & 0 & 0.8 & 0 \\ \hline Large Vehicle & 0.9 & 51 & 0.46 & 55 & 1.44 & 42.7 & 0.9 & 14 \\ \hline People Speaking & 1.25 & 18 & 0.79 & 22 & 1.29 & 8.6 & 1 & 10 \\ \hline People Walking & 0.9 & 51 & 0.48 & 57 & 1.44 & 33.5 & 1 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of results using S-CRNN classifier on Dev-Set and Eval-Set.  
Figure 11: Overall ER comparison using S-CRNN Classifier.  
Figure 10: F1-score comparison using S-CRNN Classifier.  
Figure 9: ER comparison using S-CRNN Classifier.  
We evaluated the performance of our proposed S-CRNN model and compared the overall ER and F1-score with the baseline model, as shown in Figures 11 and 12. The baseline model obtains an overall ER of 0.69 and an overall F1-score of 56.7% on the Dev-Set, whereas these scores are 42.8% and 0.93, respectively, on the Eval-Set. On the Dev-Set, the Adavanne model achieves an overall ER of 0.25 and an overall F1-score of 79.3%, whereas on the Eval-Set it achieves an overall ER of 0.79 and an overall F1-score of 41.7%. On the Dev-Set, our proposed S-CRNN model obtains an overall ER of 0.22 and an overall F1-score of 72%, whereas on the Eval- Set the overall ER is 0.68 and the F1-score is 51%. These results clearly demonstrate that the S-CRNN model increased the overall F1-score by over 10% and the ER by 0.11 on the Eval-dataset. Additionally, our proposed S-CRNN performed well on the Dev-Set, with an improved overall ER of 0.3, showing the efficacy of our proposed model.