# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 4 Experiment Results
### Experimental Result  
We perform a five-fold cross validation in ESC and a 10-fold cross validation in Urbansound8k to evaluate our model with log-mel feature input. We compared our MelNet model with existing logmel-CNN models as reported by [PERSON] [4], [PERSON] and [PERSON] [5], and [PERSON] and [PERSON] [19]. The results are presented in Table 2. First of all, with the ESC-50 dataset, the accuracy of our model (MelNet) is 81.1%, which is much higher than the 64.5% of the [PERSON] [4] model and the 66.5% of the static-delta logmel-CNN proposed by [PERSON] and [PERSON] [5]. Our algorithm performance is just slightly lower than the 81.3%, the human recognition accuracy. This is the first time that the recognition accuracy has reached over 80% using the logmel-CNN learning method on the ESC-50 dataset. On the ESC-10 dataset, the accuracy of MelNet reaches 91.4%, which is 9.9% and 4.5% higher than the accuracy (81.5%) of [PERSON]'s [4] logmel-CNN model and that of the logmel-CNN + BN model (86.8%), respectively. Finally, we evaluated the algorithms on the Urbansound8k dataset. The accuracy of MelNet is 90.2%, which is also higher than the 73.7% accuracy of [PERSON] [4] and the 79% of [PERSON] and [PERSON] [19]. These results indicate that our five-layer stacked convolutional neural network has achieved significant improvement in sound recognition with the log-mel features as input. To our knowledge, our MelNet is the most effective model for environmental sound recognition using log-mel as the feature. Next, we evaluated the performance of our RawNet CNN model that takes raw waveforms as the input of the network. By using simpler features, convolutional neural networks work by extracting feature representation from raw audio signals and then do recognition, implementing an end-to-end way for sound recognition. We evaluated the recognition performance of the RawNet on three datasets and compared the test results with the performance of existing models. On the ESC-50 dataset, our accuracy reached 66.2%, slightly higher than the 64% of [PERSON] and [PERSON]'s [5] method. On the Urbansound8k dataset, our accuracy reached 87.7%, which is higher than that achieved by [PERSON] [22], with 71.68% accuracy. This indicates that our model structure also has a better recognition accuracy for ESC with raw waveform input. Finally, we evaluated the performance of the DS-CNN, which combines the CNN model based on the log-mel feature input and raw-CNN using the raw waveform feature as the input. We used \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Accuracy (\%) on Dataset**} \\ \hline
**Model** & **Feature** & **Fusion** & **ESC-50** & **ESC-10** & **Urbansound8k** \\ \hline Logmel-EnvNet (b) [5] & Log-mel & — & 66.5 \(\pm\) 2.8 & — & — \\ SB-CNN (aug) [19] & Log-mel & — & — & — & 79 \\ Logmel + CNN [4] & Log-mel & — & 64.5 & 81.5 & 73.7 \\ Logmel + CNN + BN & Log-mel & — & 72.4 \(\pm\) 0.2 & 86.8 \(\pm\) 0.4 & 74.7 \\ MelNet (this paper) & Log-mel & — & 81.1 \(\pm\) 0.6 & 91.4 \(\pm\) 0.2 & 90.2 \(\pm\) 0.3 \\ EnvNet [5] & Raw waveform & — & 64.0 \(\pm\) 2.4 & — & — \\ M18 [22] & Raw waveform & — & — & — & 71.68 \\ RawNet (this paper) & Raw waveform & — & 65.8 \(\pm\) 0.6 & 85.2 \(\pm\) 0.4 & 87.7 \(\pm\) 0.2 \\ End-to-end ESC system [5] & Combine & average & 71.0 \(\pm\) 0.1 & — & — \\ DS-CNN (this paper) & Combine & DS evidence & 83.1 \(\pm\) 0.8 & 92.6 \(\pm\) 0.7 & 92.2 \(\pm\) 0.5 \\ Ave-CNN (this paper) & Combine & average & 81.9 \(\pm\) 0.8 & 91.5 \(\pm\) 0.3 & 91.6 \(\pm\) 0.5 \\ Pro-CNN (this paper) & Combine & product of probabilities & 82.8 \(\pm\) 1.1 & 92.1 \(\pm\) 0.6 & 91.9 \(\pm\) 0.5 \\ Human [4] & — & — & 81.3 & 96.0 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of recognition accuracy with other models on evaluated datasets. the ESC-50 and Urbansound8k datasets to compare the recognition performance before and after the model fusion. We trained the RawNet and MelNet models in a standard way.