# An Ensemble of Convolutional Neural Networks for Audio Classification
## 5 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Descriptor** & **BIRDZ** & **CAT** & **ESC-50** \\ \hline
[11] & 96.45 & 89.15 & 85.85 \\ FusionGlobal & 96.82 & 90.51 & 88.65 \\ FusionGlobal-CO & 97.00 & 90.51 & 88.55 \\
[2] & 95.1 & — & — \\
[34] & 93.6 & — & — \\
[33] & — & 87.7 & — \\
[28] & — & 91.1 & — \\
[28]–CNN & — & 90.8 & — \\
[51] & 96.7 & — & — \\
[20] & — & — & 94.10 \\
[54] & — & — & 89.50 \\
[21] & — & — & 87.10 \\
[19] & — & — & 88.50 \\
[27] & — & — & 84.90 \\
[16] & — & — & 86.50 \\
[17] & — & — & 83.50 \\
[52] & — & — & 83.50 \\
[18] & — & — & 81.95 \\ Human Accuracy [4] & — & — & 81.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our best sound classification ensemble with state of the art. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CAT**} & \multicolumn{3}{c}{**BIRD**} & \multicolumn{3}{c}{**ESC-50**} \\ \hline  & **GA** & **MEL** & **CO** & **GA** & **MEL** & **CO** & **GA** & **MEL** & **CO** \\ \hline AlexNet & 82.03 & 83.73 & 79.32 & 91.85 & 91.43 & 87.54 & 73.95 & 73.50 & 65.50 \\ GoogleNet & 74.07 & 84.07 & 77.97 & 90.71 & 88.96 & 86.95 & 73.75 & 73.25 & 66.15 \\ VGG16 & 83.39 & 86.10 & 80.00 & 92.65 & 93.17 & 88.82 & 77.60 & **79.20** & 66.75 \\ VGG19 & 85.76 & 83.73 & 77.97 & 92.93 & 93.22 & 89.07 & 76.40 & 77.55 & 65.85 \\ ResNet50 & 82.03 & 83.05 & 75.93 & 90.87 & 90.74 & 86.98 & 75.80 & 76.05 & 67.75 \\ ResNet101 & 82.71 & 82.37 & 79.32 & 91.15 & 91.00 & 87.28 & 75.00 & 74.80 & 64.90 \\ Inception & 79.66 & 84.75 & 77.63 & 89.53 & 89.86 & 87.35 & 73.95 & 72.55 & 67.50 \\ GoogleNetPlaces365 & 83.05 & 82.71 & 77.63 & 90.88 & 88.31 & 86.75 & 73.60 & 75.50 & 68.70 \\ VGG16 BatchSize & 85.42 & **87.80** & 81.02 & 93.09 & **93.22** & 89.43 & 77.80 & 78.95 & 67.50 \\ FusionLocal & 87.46 & **88.47** & 82.37 & 93.76 & **93.97** & 90.57 & 81.90 & **83.80** & 73.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance using different methods for representing the signal as an image. The following conclusions can be drawn from the reported results:  
1. There is no single data augmentation protocol that outperforms all the others across all the tests. TSM performs best on CAT and ESC-50 but works poorly on BIRDZ. Data augmentation at the spectrogram level works poorly on ESC-50 as well as on two other datasets. SGN and data augmentation at the signal level work well across all the datasets. On average, the best data augmentation approach is SSA. Although it produces a performance that is close to SSiA, the training time for SSA is shorter;
2. The best stand-alone CNNs are VGG16 and VGG19;
3. DGT works better than the other signal representations;
4. Combining different CNNs enhances performance across all the tested datasets;
5. For the ensemble FusionLocal, data augmentation is marginally beneficial on CAT and BIRDZ but produces excellent results on ESC-50. Compared to the stand-alone CNNs, data augmentation improves results on all three datasets. Of note, an ensemble of VGG16 (FusionSuperVGG16) outperforms the stand-alone VGG16;
6. The performance of the ensemble of CNNs trained with different augmentation policies (FusionALL) can be further improved by adding to the ensemble those networks trained using different signal representations (FusionGlobal). However, this performance improvement required considerable computation time, mainly during the training step;
7. The approach in [20] manages to outperform our results, but the authors pretrained their networks on AudioSet; hence the comparison with our approach is not fair. The methods reported in Table 6 were based solely on deep learning approaches. As mentioned in the introduction, several papers have proposed sound classification methods based on texture features. It is also possible to construct ensembles that combine deep learning with texture methods. To examine the potential of combining ensembles trained on texture descriptors with deep learning approaches, the following two fusion rules were examined:  
1.