# Detecting Selected Instruments in the Sound Signal
## 5 Used Architectures and Experimental Results
### Convolutional Neural Networks
The network was built according to the diagram in Figure 3, replacing the activation function _ReLU_ with _LeakyReLU_, and the training process was started based on normalized input data. This network architecture is marked as Model CNN6 in Table 5. The obtained efficiency was 63%, and the confusion matrix indicates that the distribution of accuracy among individual instruments has not changed. Table 5 summarizes the performance of various _CNN_ network configurations. Analyzing Table 5, it can be concluded that the CNN4 model has the highest F1-score efficiency. This network achieved an accuracy of 68% and a F1-score of 65%. The confusion matrix associated with the results of this model is similar to all other models tested to date. The networks achieve the best results for five classes: piano, singing, guitar, trumpet, and violin. The remaining three, i.e., clarinet, saxophone, and flute, are correctly assigned much less often--the accuracy for these instruments does not exceed 50%. The clarinet and flute are usually confused with the piano. The model more often defines these instruments as pianos than labels them correctly. However, the saxophone is accurately classified the least frequently for most of the tested models. The vast majority of saxophone recordings are labeled as electric guitars. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drop-Out Rate** & **Accuracy** & **Sensitivity** & **Precision** & **F1-Score** \\ \hline
0.1 & 0.68 & **0.63** & 0.58 & 0.60 \\ \hline
0.2 & **0.69** & 0.61 & **0.64** & **0.62** \\ \hline
0.3 & 0.65 & 0.59 & 0.59 & 0.59 \\ \hline
0.4 & 0.61 & 0.51 & 0.57 & 0.54 \\ \hline
0.5 & 0.56 & 0.47 & 0.53 & 0.50 \\ \hline \hline \end{tabular} For each evaluation parameter, the best result obtained is presented in bold.  
\end{table} 
Table 4: The impact of the _drop-out_ value on the efficiency of the convolutional network. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Norm. * & **Drop-Out** & **Accuracy** & **Sensitivity** & **Precision** & **F1-Score** \\ \hline CNN1 & No & 0.1 & 0.66 & 0.66 & 0.62 & 0.64 \\ \hline CNN2 & No & 0.1 & 0.68 & 0.63 & 0.58 & 0.60 \\ \hline CNN3 & No & 0.2 & **0.69** & 0.61 & **0.64** & 0.62 \\ \hline CNN4 & Yes & 0.1 & 0.68 & **0.67** & 0.63 & **0.65** \\ \hline CNN5 & Yes & 0.2 & 0.64 & 0.63 & 0.58 & 0.60 \\ \hline CNN6 & Yes & 0.1 & 0.63 & 0.64 & 0.63 & 0.63 \\ \hline \hline \end{tabular} For each evaluation parameter, the best result obtained is presented in bold. * Normalization of input data.  
\end{table} 
Table 5: Efficiency of various convolutional neural networks. Similar results were achieved by the CNN3 model. It has the highest accuracy of all models at 69%, but the F1-score is 3% lower than the CNN4 Model. Basically, the results achieved are satisfactory. Most well-configured models produce results in the range of 0.6-0.7 for both accuracy and F1-score. Taking into account only the group of five instruments mentioned above, the overall efficiency of the models would be much higher. Therefore, in order to significantly improve the current results, the way of classifying the clarinet, flute, and saxophone should be corrected.