# An Ensemble of Convolutional Neural Networks for Audio Classification
## 5 Experimental Results
### Results  
In Tables 2-5, the accuracy obtained by some of the data augmentation protocols is reported and compared with the baseline that skips the augmentation step (NoAUG). The CNNs were trained for 30 epochs with a learning rate of 0.0001, except for the last fully connected layer that has a learning rate of 0.001 and a batch size of 60. The one exception is the CNN labeled 'VGG16 BatchSize', the standard VGG16 with a fixed batch size of 30. For NoAUG, the batch size was set to 30. Also, seven fusions are reported in Tables 2-5. We combined the results of the CNNs in an ensemble using the sum rule. The sum rule consists of averaging all the output probability vectors of the stand-alone CNNs in the ensemble to create a new probability vector that is used for classification. The rationale behind fusion, as [PERSON] (2018) notes, is that \"the collective decision produced by the ensemble is less likely to be in error than the decision made by any of the individual networks.\" The labels used in the tables and a brief description of the seven ensembles follow:  
1. GoogleGoogle365: sum rule of GoogleNet and GoogleNetPlaces365 trained with each of the data augmentation protocols;
2. FusionLocal: sum rule of CNNs where each one is trained with a different data augmentation method;
3. FusionShort: sum rule of all CNNs trained with SGN, SSA, and SSpA;
4. FusionShortSuper: sum rule of all CNNs trained with SGN, SSA, SSpA, SSpA, and SuSA;
5. FusionSuper: sum rule of all CNNs trained with SGN, SSA, SuSA, and TSM;
6. FusionSuperVGG16: sum rule of VGG16 trained with SGN, SSAi, SuSA, and TSM;
7. FusionALL: sum rule of all CNNs trained with SGN, SSA, SSpA, SSAi, SuSA, and TSM. Figure 4: Spectrogram samples from **(a)** BIRDZ, **(b)** CAT, and **(c)** ESC-50 illustrating the applications of identifying bird songs, cat calls, and environmental noise. The horizontal axis in each image represents time, and the vertical axis represents frequency. Note that the frequency scale has been reversed in this figure. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ESC-50** & **NoAUG** & **SGN** & **SSA** & **SSpA** & **SSiA** & **SuSA** & **TSM** \\ \hline AlexNet & 60.80 & 72.75 & 73.85 & 65.75 & 73.30 & 64.65 & 70.95 \\ GoogleNet & 60.00 & 72.30 & 73.70 & 67.85 & 73.20 & 71.70 & 73.55 \\ VGG16 & 71.60 & 79.40 & **80.90** & 75.95 & 79.35 & 77.85 & 79.05 \\ VGG19 & 71.30 & 78.95 & 78.80 & 74.10 & 78.00 & 76.40 & 77.45 \\ ResNet50 & 62.90 & 76.65 & 75.95 & 70.65 & 77.20 & 73.95 & 77.40 \\ ResNet101 & 59.10 & 75.25 & 75.65 & 70.05 & 77.50 & 72.30 & 74.85 \\ Inception & 51.10 & 71.60 & 74.70 & 63.45 & 75.55 & 71.10 & 70.65 \\ GoogleNetPlaces365 & 63.60 & 75.15 & 76.10 & 71.35 & 74.00 & 71.60 & 73.55 \\ VGG16 BatchSize & — & 79.40 & 80.50 & 73.45 & 79.35 & 77.85 & 80.00 \\ FusionLocal & 75.95 & 84.75 & 85.30 & 80.25 & 85.25 & 82.25 & 85.30 \\ FusionShort & & & & & & & \\ FusionShortSuper & & & & & & & \\ FusionSuper & & & & & & & \\ FusionALL & & & & & & & \\ FusionSuperVGG16 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance on the ESC-50 (mean accuracy over the five-fold cross-validation). Values of interest are in bold.