# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 4 Experiment Results
### Experimental Result
Then, each segment in the sound file was predicted, which output the softmax values. These two softmax values of the two models were assigned as the basic probability assignment \(m_{1}\), \(m_{2}\). Then, \(m_{1}\) and \(m_{2}\) are fused by the orthogonal operation. In this experiment, we still used the k-fold cross validation (k = 5 for ESC-50, k = 10 for Urbansound8k) to evaluate the performance of our DS-CNN method. The experimental results of the DS-CNN on the ESC-50 and Urbansound8k datasets are shown in Table 2. We find that when the DS evidence theory is used to merge these two models, the accuracy has increased by 2% compared with the logmel-CNN method, reaching 83.1%, which is higher than the recognition accuracy (81.3%) of the human. This demonstrates that the raw waveform feature is capable of complementing the logmel feature in ESC by providing information that is otherwise neglected by the logmel feature. On the ESC-50 dataset, the accuracy of DS-CNN, designed by us, is 12.1% higher than that of the ensemble model proposed by [PERSON] [5], which proves the superiority of our model in dealing with the environmental sound event recognition. In order to further demonstrate whether the DS-CNN outperforms RawNet and MelNet in a statistically significant way, we added the experimental results from paired accuracy statistics and applied a paired sample \(t\)-test. Table 3 shows the performance achieved by the three models with ESC-50. The accuracy scores of the single models are 65.7% and 81.09%, obtained by RawNet and MelNet, respectively. Moreover, for the fusion model (DS-CNN), the accuracy is 83.09%. The results demonstrate that the fusion model (DS-CNN) outperforms both RawNet and MelNet. The results for the two paired sample \(t\)-tests of accuracies obtained by DS-CNN, RawNet, and MelNet are summarized in Table 4. On average, the DS-CNN has improved the performances of RawNet and MelNet by 18% and 2%, respectively. The two-tailed \(p\)-values approach zero (\(p<0.05\)) for each compared pair, which means that the DS-CNN is better than RawNet and MelNet in a statistically significant sense. It can also be seen from Table 4 that the \(p\) values indicate that the DS fusion method has a significant effect on the accuracy. In addition, we further compared the performance of DS evidence fusion with other commonly used fusion methods, such as averaging and product of probabilities [6]. The comparison results of these fusion methods are shown in Table 2, which demonstrate the advantage of DS evidence fusion compared to other fusion methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \hline \multirow{4}{*}{Pair 1} & DS-CNN-RawNet & 18.2020 & 0.21971 & 0.09825 & 2.09301 & 1.54739 & 18.525 & 4 & 0.000 \\  & DS-CNN-MelNet & 1.9060 & 0.04133 & 0.01848 & 2.41920 & 0.13928 & 10.312 & 4 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of two pairs of algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & & \\ \hline \hline \multirow{4}{*}{Pair 1} & DS-CNN & 0.8309 & 5 & 0.00882 & 0.00395 \\  & RawNet & 0.6579 & 5 & 0.00640 & 0.00286 \\ \hline \multirow{2}{*}{Pair 2} & DS-CNN & 0.8309 & 5 & 0.00882 & 0.00395 \\  & MelNet & 0.8109 & 5 & 0.00577 & 0.00258 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of accuracy achieved by the three models.