## 5 Results and Discussion  
In the first experiment, three CNNs were trained using databases of the first type, i.e., four separate databases consisting of spectrograms obtained for different window sizes. Once the CNNs were trained, they were tested using a test dataset (each network individually) and then the average accuracy value was calculated. Table 5 collects the averaged accuracy results for each network evaluated on each database. Figure 7 is a graphic interpretation of the results shown in Table 5.  
As can be seen, the differences obtained in relation to the absolute accuracy values are definitely low. Nevertheless, the highest overall accuracy value was achieved for data processed with a window size of 256. The lowest accuracy value was obtained for data processed using a window size of 1024. The difference in accuracy values between the results obtained using the window of size 256 and 1024 is 1.53%. When testing with data created using a window other than the data the network was trained on, the highest accuracy was achieved for the network trained on data generated using a window of 128 and then tested on data generated using a window of 256. Nevertheless, a similar pattern can be observed in all analyzed cases. The second-highest score in terms of accuracy was achieved by testing spectrograms generated using a window one size larger than the base size. This is probably related to the proximity of the spectrogram's distributions achieved for both sizes, as well as the fact that a larger window size does not cause further generalization of information (simplification of characteristics). The absolute smallest \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Name** \\ \hline Training Algorithm & RMSProp \\ Initial Learning Rate & 0.005 \\ Learning Rate Schedule & Piecewise \\ Learning Rate Drop Factor & 0.1 \\ Learning Rate Drop Period & 10 Epoch \\ Number of Epoch & 80 \\ Batch Size & 256 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters used for the training process of the CNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Version of Sub-Database Used to Train the CNN:**} \\ \cline{2-5}  & & **128** & **256** & **512** & **1024** \\ \hline \multirow{3}{*}{**Version of**} & 128 & 75.28\% & 7.04\% & 1.09\% & 0.82\% \\ \cline{2-5}  & 256 & 13.73\% & 75.77\% & 5.14\% & 1.21\% \\ \cline{2-5}  & 512 & 4.79\% & 12.74\% & 75.28\% & 5.21\% \\ \cline{2-5}  & 1024 & 2.71\% & 3.85\% & 9.18\% & 74.24\% \\ \hline Mean accuracy value & 24.12\% & 24.85\% & 22.67\% & 20.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracies of the trained CNNs verified with various versions of the test set. change in window size occurs between sizes 128 and 256, which likely explains the highest testing performance at a window of 256 for networks trained for a data size of 128. Taking a further perspective on the generalization of the input data by a given network, considering a quantitative description made by means of a mean accuracy value, a network trained on data using a window of 256 is able to obtain the highest rate. However, it should be noted that the differences achieved between the classes are small and do not enable an unambiguous assessment. Another experiment was then performed in which three CNNs were trained using a second major database, which contains the spectrograms generated with all four window sizes (combining all sub-databases). The trained networks were tested on both sets of databases (individual ones and combined one) and the results of the average performance are summarized in Table 6.  
As can be seen, for a test set containing data produced with all window sizes, an accuracy of 70.06% was achieved. Again, the differences in the accuracies obtained during testing with individual databases are small. The lowest accuracy value was obtained for the test set made with a window size of 1024. The highest accuracy was obtained for the database made using a window size of 256.