## 5 Results and Discussion
### Event Detection with Localization  
Table 5 presents the segment-level results on the DCASE 2017 task 4 evaluation set. Both the DSNet and DSNet-RNN outperformed the baseline CNN model in F1 score by 0.0148 and 0.0367, respectively. Similar to the clip-level results, the DSNet performed better than conventional CNN by using DenseNet and SENet. Especially, the DSNet-RNN showed the best performance in segment-level results. This indicates that each segment-level prediction benefits from considering contextual information in the neural network.  
The weight \(\lambda\) introduced in Equation (8) is a hyperparameter, which allows us to control the dependency of the cost function on structured prediction. The effect of the weight \(\lambda\) on the DSNet-RNN is presented in Table 6. The result shows that, when \(\lambda=0\), the model did not show significant performance improvement over the DSNet. This means that the flow of uncertain information in the RNN may hinder the training of the model in weakly supervised learning. The overall results show that the performance of the model could be improved by restricting uncertain information flow with appropriate constraints based on prior information. The model had the best performance when \(\lambda=0.01\), which was used in training the DSNet-RNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **CNN** & **DSNet** & **DSNet-RNN** \\ \hline Train horn & 0.5273 & 0.4615 & 0.5102 \\ Air horn, truck horn & 0.4000 & 0.5455 & 0.5783 \\ Car alarm & 0.4267 & 0.4500 & 0.3836 \\ Reversing beeps & 0.3373 & 0.3765 & 0.4186 \\ Ambulance & 0.5556 & 0.4681 & 0.4854 \\ Police car & 0.4960 & 0.5778 & 0.6525 \\ Fire engine, fire truck & 0.5606 & 0.6055 & 0.5586 \\ Civil defense siren & 0.7704 & 0.8160 & 0.8189 \\ Screaming & 0.6833 & 0.7059 & 0.8333 \\ Bicycle & 0.4675 & 0.4615 & 0.3294 \\ Skateboard & 0.5946 & 0.7627 & 0.6372 \\ Car & 0.6266 & 0.6759 & 0.6411 \\ Car passing by & 0.2727 & 0.2931 & 0.2468 \\ Bus & 0.4238 & 0.4000 & 0.2637 \\ Truck & 0.4455 & 0.4541 & 0.4505 \\ Motorcycle & 0.5465 & 0.6324 & 0.7009 \\ Train & 0.7209 & 0.7883 & 0.7759 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Class-wise clip-level F1 score results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **F1** & **P** & **R** \\ \hline CNN & 0.5506 & 0.5667 & 0.5353 \\ DSNet & 0.5853 & 0.5822 & 0.5883 \\ DSNet-RNN & 0.5839 & 0.5504 & 0.6281 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clip-level results on the DCASE 2017 task 4 evaluation set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **F1** & **P** & **R** \\ \hline CNN & 0.4987 & 0.4598 & 0.5447 & 0.7568 \\ DSNet & 0.5135 & 0.4746 & 0.5593 & 0.7039 \\ DSNet-RNN & 0.5354 & 0.5074 & 0.5667 & 0.6213 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Segment-level results on the DCASE 2017 task 4 evaluation set.  
Table 7 presents the influence of CRF post-processing on the segment-level performances. All models show performance improvement through CRF post-processing. In the DSNet-RNN, the performance improvement was relatively low. This indicates that the DSNet-RNN already reflected contextual information and hence the additional benefit from CRF post-processing is relatively small. The results of the DSNet-RNN with and without CRF post-processing are visualized in Figure 2. We could correct isolated inaccurate predictions and improve the predictions particularly in the boundaries of the events by employing CRF post-processing.