# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 5 Discussion
### When and How the DS Evidence Fusion Improves the Recognition Performance  
To explore how the information fusion by DS helps to improve the performance, we created the confusion matrices of the DS-CNN, Mel-Net, and RawNet models on ESC-50 and compared the performance improvements of the DS-CNN over the other two models. Due to page limitation, we only show the confusion matrix of the DS-CNN in Figure 4 and list the comparison of recognition accuracy for all sound types before and after DS fusion in Figure 5. For the dataset Urbansound8k, all three confusion matrices are shown in Figure 6.  
We observed that the recognition accuracy changes for each sound type, as shown in the confusion matrix before and after combination. The DS-CNN has improved the accuracy for 28 kinds of environmental sounds to varying degrees. The recognition performance over the remaining 22 kinds \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Paired Accuracy Test**} \\ \hline  & & & \multicolumn{6}{c}{**Paired Difference**} & & & \\ \hline  & & \multicolumn{3}{c}{**Mean**} & \multicolumn{3}{c}{**Std.**} & \multicolumn{3}{c}{**Std. Error Mean**} & \multicolumn{3}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{\(\mathbf{t}\)} & \multirow{2}{*}{**df**} & \multicolumn{1}{c}{**Sig.**} \\  & & \multicolumn{3}{c}{**Deviation**} & & & & **Lower** & & & \\ \hline \multirow{2}{*}{**RawNet**} & Pair 1 & 0.03086 & 0.01150 & 0.00514 & 0.01658 & 0.04514 & 6.000 & 4 & 0.004 \\  & Pair 2 & 0.02370 & 0.00497 & 0.00222 & 0.01752 & 0.02987 & 10.650 & 4 & 0.000 \\ \hline \multirow{2}{*}{**MelNet**} & Pair 1 & 0.01772 & 0.12406 & 0.00555 & 0.00232 & 0.03313 & 3.194 & 4 & 0.033 \\  & Pair 2 & 0.01878 & 0.13767 & 0.00616 & 0.00169 & 0.03587 & 3.050 & 4 & 0.038 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy \(t\)-test results of four different filter size pairs. of environmental sounds are the same as that of the MelNet model. This implies that the combination model is capable of complementing log-mel features by learning higher level features from the raw waveform via the RawNet to help improve the performance over the 28 kinds of environmental sounds. We further compared the accuracy of the DS-CNN with that of MelNet and RawNet on 28 environmental sound types, and present the comparison results in Figure 5. After comparison, we noticed that the recognition accuracy of the combination model obviously increases on human nonspeech and urban noises. The accuracy on seven out of 10 kinds of environmental sounds in each category has increased with the average increase rate of 6% (over urban noises) and 3.85% (over human nonspeech). Considering that these two kinds of sounds have the characteristics of continuous stability, such as for sneezing and engines, it suggests that the log-mel feature input ignores some potentially differentiating features of the continuous stability sounds. Similarly, for animal and domestic sound types, the accuracy of the DS-CNN has improved accuracy for five out of 10 different kinds of sounds, with the average increase rate of 4% and 5.8%, respectively. Moreover, these two kinds of sounds are all single sounds, which indicates that some features in the single sound can also be neglected by log-mel features. Using the DS-CNN, we can make use of features that RawNet learned from raw waveforms to complement the log-mel feature, so that the recognition accuracy can be improved via information fusion. Figure 4: Confusion matrix for the DS-CNN evaluated on the ESC-50 dataset. The experimental results on the Urbansound8k dataset also verify our conclusion. The average accuracy of the 10-fold cross-validation on the MelNet and RawNet models before model combination was 90.2% and 82.8%, respectively. After model combination, the accuracy reaches 92.2%. Figure 5(a)-c shows the confusion matrixes of the MelNet, RawNet, and fusion models. The diagonal elements of each matrix show the accuracy of predicting sounds of 10 classes.