# An Ensemble of Convolutional Neural Networks for Audio Classification
## 5 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CAT** & **NoAUG** & **SGN** & **SSA** & **SSpA** & **SSiA** & **SuSA** & **TSM** \\ \hline AlexNet & 83.73 & 85.76 & 86.10 & 83.39 & 87.12 & 86.78 & 87.12 \\ GoogleNet & 82.98 & 86.10 & 87.80 & 83.39 & 86.78 & 85.08 & 87.80 \\ VGG16 & 84.07 & 87.12 & 88.47 & 85.76 & 87.80 & 87.80 & 88.47 \\ VGG19 & 83.05 & 85.42 & 87.80 & 84.75 & 86.10 & 86.10 & 89.15 \\ ResNet50 & 79.32 & 81.36 & 85.42 & 76.95 & 85.08 & 82.03 & 87.12 \\ ResNet101 & 80.34 & 84.75 & 85.42 & 75.59 & 82.03 & 73.56 & 86.78 \\ Inception & 79.66 & 82.71 & — & 66.44 & — & 84.07 & 86.10 \\ GoogleNetPlaces365 & 85.15 & 86.44 & 85.76 & 83.73 & 86.10 & 86.10 & 88.47 \\ VGG16 BatchSize & — & 86.10 & 88.14 & 86.78 & 89.49 & 86.10 & **89.15** \\ FusionLocal & 88.14 & 88.47 & 89.83 & 86.78 & 89.83 & 89.83 & **90.51** \\ FusionShort & & & & & & & \\ FusionSuper & & & & & & & \\ FusionALL & & & & & & & \\ FusionSuperVGG16 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance on the CAT (mean accuracy over ten-fold cross-validation). Values of interest are in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BIRDZ** & **NoAUG** & **SGN** & **SSA** & **SSpA** & **SSiA** & **SuSA** & **TSM** \\ \hline AlexNet & 94.48 & 94.96 & 95.40 & 94.02 & 95.05 & 95.76 & 88.51 \\ GoogleNet & 92.41 & 94.66 & 94.84 & 91.48 & 93.85 & 95.85 & 82.91 \\ VGG16 & 95.30 & 95.59 & 95.60 & 94.69 & 95.44 & 96.18 & 94.63 \\ VGG19 & 95.19 & 95.77 & 87.15 * & 94.50 & 95.44 & 96.04 & 94.88 \\ ResNet50 & 90.02 & 94.02 & 93.22 & 90.48 & 92.95 & 94.16 & 91.75 \\ ResNet101 & 89.64 & 94.00 & 92.76 & 88.36 & 92.84 & 94.20 & 90.62 \\ Inception & 87.23 & 93.84 & 92.48 & 83.81 & 92.30 & 94.01 & 90.52 \\ GoogleNetPlaces365 & 92.94 & 94.81 & 95.10 & 92.43 & 94.76 & 95.80 & 86.91 \\ VGG16 BatchSize & — & 95.84 & — & 94.91 & 95.81 & **96.31** & 94.78 \\ FusionLocal & 95.81 & 96.32 & 96.24 & 95.76 & 96.39 & **96.89** & 95.27 \\ FusionShort & & & & & & & \\ FusionShort & & & & & & & \\ FusionShort & & & & & & & \\ FusionShortSuper & & & & & & & \\ FusionAll & & & & & & & \\ FusionAll & & & & & & & \\ FusionSuperVGG16 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance on the BIRDZ (mean accuracy over ten-split training/test set). VGG16 can fail to converge; when this happens, VGG16 undergoes a second training. VGG16 can also produce a numeric problem by assigning the same scores to all patterns. In this case, all scores are considered zeros. Other numeric problems in the fusions by sum rule can occur. To avoid such issues, all scores that do not produce not-a-number value are treated as zero. In Tables 2-4, the DGT spectrogram is used for representing the signal as an image. Any cell with '--' means that the given CNN was not trained successfully (mainly due to memory problems with the graphical processing units (GPUs). We also tested three additional methods GA, MEL, and CO to represent a signal as an image, coupled with SGN only, as reported in Table 5.  
In Table 6, our best ensembles FusionGlobal and FusionGlobal-CO are compared with the state of the art. FusionGlobal is built with the CNNs belonging to Fusion Super and those reported in Table 5. FusionGlobal-CO is constructed similarly to FusionGlobal but without considering the CNNs trained using CO as a signal representation approach. The performance reported in [11] in Table 5 is different from that reported in the original paper since, for a fair comparison with this work, we ran the method without considering the supervised data augmentation approaches.