## 7 Discussion of the Results Based on Selected Quality Criteria  
Despite the lack of numerous examples in the professional literature about the possibility and effectiveness of the use of neural networks in the issue of identifying musical notation symbols on a staff, it has been proven during the research that it is a quite good approach. The study of note recognition was subject to quality criteria. As a result of the test carried out on the number of one thousand randomly selected symbol patterns, the network efficiency was 96.8%. This is a highly satisfactory result. This method, however, has a very significant disadvantage, which is it is time-consuming and has a high average time of recognizing one note, amounting to 1.086 s (Table 1). Its unquestionable advantage is the features of neural networks, which, due to their nature of the operation, can surpass classical algorithms. The reason for this is related to the undoubted feature of the network, which is its ability to generalize. Unfortunately, this does not occur in the classic algorithm with an expert approach.  
A study conducted on the same thousand patterns with the use of an algorithm that does not use neural networks shows that it coped very well with the task of classifying notes. Its effectiveness is 98.7% (Table 1). This may be due to the good preparation of an expert approach to the issues of recognition and classification. The disadvantage of this solution is related to the lack of generalization skills. The advantage of this algorithm is its very fast time of operation, as the average time of 0.018 s was devoted to the analysis of a single image. It is nearly 60 times faster than the first method of identification. Taking into account the time of starting the algorithm, a runtime of 1101.74 s was observed for a solution including a neural network. The second approach took 21.57 s.  
The research conducted on the classification of notes on a staff offers important conclusions. The method using artificial neural networks is significantly different from the classical identification algorithm. Each method has its advantages and disadvantages. The classic algorithm for the given case did a great job, as did the neural network. Both  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Classification Time (s)** & **Accuracy (\%)** \\ \hline Neural Network RBF & 1085.56 & 96.8 \\ \hline Classical algorithm & 18.02 & 98.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of the quality criteria for the classification of 1000 notes.  
Figure 19: Procedure for image segmentation into eight areas [source: authors].  
classification systems work very satisfactorily. However, it can be anticipated that the expert approach may not give good results in the case of a change in the classified photos (e.g., a change in the musical note environment). The reason is the lack of generalization, which is a feature of the neural network. However, it is worth noting that the appropriate preparation of the environment for the classical classification system will certainly produce good results in the quality indicators. This approach may require more work and time. However, such an algorithm will be very fast and accurate (Table 1).  
The neural network performs very well in the tasks of recognizing more complex and complicated shapes, which is confirmed by Table 1. Thus, it can be stated that the proposed approach is a suitable tool for the task of classifying music notation.  
In the last chapter, the conclusions drawn from the research will be summarized. In addition, plans for further testing and study will be described.