# Animal Sound Classification Using Dissimilarity Spaces
## 5 Experimental Results  
The proposed approach is tested and compared with the canonical approaches with each using the testing protocol proposed by the authors of the datasets. The performance indicator is the classification accuracy and methods were tested on the following animal vocalization datasets:  
* BIRDz, which functioned as a control and a real-world audio dataset in [46], a ten-run testing protocol is used; we have used the same split used by the authors of the dataset. The real-world tracks were collected from the Xeno-canto Archive ([[http://www.xeno-canto.org/](http://www.xeno-canto.org/)]([http://www.xeno-canto.org/](http://www.xeno-canto.org/))). _BIRDz_ includes a total of 2762 bird acoustic samples from 11 North American bird species plus 339 \"unknown\" samples that include noise and unknown species' vocalizations. The observations are composed of five different spectrograms: 1) constant frequency, 2) frequency modulated whistles, 3) broadband pulses, (4) broadband with varying frequency components, and 5) strong harmonics. The dataset is balanced: the size of all the \"bird\" classes varies between 246 and 259; only the class \"other\" is a little larger. * CAT, [37; 47] is a dataset that contains ten balanced classes of approximately 300 samples per class for a total of 2962 samples. The testing protocol is a 10-fold cross-validation. The ten classes represent the following cat vocalizations: (1) Resting, (2) Warning, (3) Angry, (4) Defense, (5) Fighting, (6)-Happy, (7) Hunting mind, (8) Mating, (9) Mother call, and (10) Paining. The average duration of each sample is approximately 4 s. Samples were garnered from such online resources as Kaggle, Youtube, and Flickr. In this section, we report experiments aimed at evaluating the proposed system by varying several components: i.e., the input images (spectrograms or HASC images), the topology of the Siamese Network (NN1, NN2, NN3, NN4), the clustering algorithm (K-Means, K-Medoids, Hierarchical, Spectral), the clustering modality (unsupervised or supervised, i.e., clustering on the whole training set or on each class), and the number of prototypes (\(kc\) = 15, 30, 45, 60). For the training of the Siamese networks in our experiments, we have selected the training options suggested by the MATLAB framework for Siamese networks. This choice ensures that such values have not been overfitted on the selected dataset. The binary cross-entropy gives the loss function between the predicted score and the true label value. The parameters for ADAM optimization are the following: learning rate 0.0001, gradient decay factor 0.9, squared gradient decay factor 0.99. The number of iterations has been set at 3000 (without any early stop criterion). In the first experiment, reported in Table 2, only K-means clustering is explored. The results are obtained from the fusion by sum rule of the four SVMs trained using the dissimilarity spaces built with different values for \(kc\) (15, 30, 45, 60). Performance is reported for only NN1 and NN2 (the first two network topologies) to reduce the computation time. The ensembles in Table 2 are obtained by varying the input data (Sp = spectrograms, HASC = HASC images), the type of clustering (unsupervised or supervised), and the network topology. The clustering method is fixed to K-means for all the methods, and the number of prototypes belongs to the following set (15,30,45,60). The column labeled #_classifiers_ recaps the number of classifiers in the ensemble, and the first column (\"Name\") assigns a name to the ensemble. The best average performance is obtained by the ensemble FA1_2 in the last row (which is the sum rule of the methods in the first eight rows). On the BIRD dataset, there is a boost in performance with NN1 and NN2 using the HASC images instead of the spectrograms, while on the CAT dataset, HASC images boost the performance of NN2 but not NN1. The goal of the second experiment is to compare the clustering methods.