# Animal Sound Classification Using Dissimilarity Spaces
## 5 Experimental Results

Table 2: Performance obtained by k-means clustering: accuracy \(\pm\) standard deviation. an ensemble obtained by retraining Siamese HSup-1 (i.e., by using the same approach as that reported in Table 2) is compared with ensembles obtained by varying the network topology. The results of Table 5 indicate that changing the network topology introduces diversity in the ensemble: the comparison among ensembles of 4, 8, and 16 networks (see HSup-1x), obtained from the topology NN1, and the ensembles named F_NN, obtained by varying the topology of the Siamese Network, show an evident performance gain in favor of the latter (with the same number of classifiers). It is also interesting to observe the similar results in rows 2 and 3: both ensembles have four networks, but the first is made simply by retraining the same model, while the second has different numbers of prototypes. Thus, varying the values of \(kc\) is not very important when building an ensemble. For obtaining superior performance, it is necessary to vary the network topologies. Finally, reported in Table 6 is a comparison between the Siamese networks and standard CNNs trained with spectrograms. The method labeled eCNN is the fusion among different CNNs (GoogleNet, VGG16, VGG19, and GoogleNetP365). From the results reported in the previous tables, the following conclusions can be drawn:  
* The best way for building an ensemble of Siamese networks is to combine different network topologies;
* The proposed F_NN ensemble improves previous methods based on Siamese networks (cf. OLD in Table 6);
* F_NN obtains a performance that is similar to eCNN on BIRD but lower than eCNN on CAT;
* The best performance in both datasets is gained by sum rule between eCNN and F_NN (i.e., the fusion among CNNs and the Siamese networks). As far as the computation requirements are concerned, using a Xeon E5-1603 v4-2.8 GHz-64 GB Ram, the proposed approach requires a classification time of less than 0.028 s per a batch of 1251 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **CAT** & **BIRD** \\ \hline OLD [43] & 82.41 & 92.97 \\ F\_NN & 84.07 & 94.99 \\ \hline GoogleNet & 82.98 & 92.41 \\ VGG16 & 84.07 & 95.30 \\ VGG19 & 83.05 & 95.19 \\ GoogleNetP365 & 85.15 & 92.94 \\ eCNN & 87.36 & 95.81 \\ OLD + eCNN & 87.76 & 95.95 \\ F\_NN + eCNN & **88.47** & **96.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance obtained considering different standard CNN. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & \begin{tabular}{c} **Input** \\ **Image** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Topology** \\ \end{tabular} & \
\begin{tabular}{c} **Clustering** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Clustering** \\ **Type** \\ \end{tabular} & \#Prototypes** & \#Classifiers** & **CAT** & **BIRD** \\ \hline HSup-1(1) & HASC & NN1 & K-means & S & 15 & 1 & 75.93 ± 1.5 & 93.92 ± 0.85 \\ \hline HSup-1(4) & HASC & NN1 & K-means & S & 15 & 1\(\times\)4 & 81.69 ± 1.3 & 94.50 ± 0.78 \\ \hline HSup-1 & HASC & NN1 & K-means & S & 15, 30, 45, 60 & 4\(\times\)1 & 78.64 ± 1.2 & 94.52 ± 0.65 \\ \hline HSup-1(8) & HASC & NN1 & K-means & S & 15, 30, 45, 60 & 4\(\times\)2 & 80.68 ± 1.1 & 94.56 ± 0.75 \\ \hline HSup-1(16) & HASC & NN1 & K-means & S & 15, 30, 45, 60 & 4\(\times\)4 & 81.02 ± 1.0 & 94.63 ± 0.77 \\ \hline F\_NN(4) & HASC & All & K-means & S & 15 & 4 & 83.39 ± 0.9 & 94.73 ± 0.62 \\ \hline F\_NN(8) & HASC & All & K-means & S & 15, 30 & 8 & **84.07** ± 0.8 & 94.90 ± 0.60 \\ \hline F\_NN & HASC & All & K-means & S & 15, 30, 45, 60 & 16 & **84.07** ± 0.8 & **94.99** ± 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between ensembles of reiterated Siamese Networks with NN1 and ensembles obtained considering different network topologies: accuracy ± standard deviation. images in the testing phase, the creation of the test dissimilarity space requires 44.30 s per a batch of 1251 images, and Hasc needs 0.006 s to be performed in a given spectrogram.