# Technical, Musical, and Legal Aspects of an AI-Aided Algorithmic Music Production System
## 3 Results and Discussion
### Results of the Generator-Critic System Operation
With each repetition of this sequence, phrases in sections _L1_ and _L2_ are altered. Our hybrid AI system may be seen as more versatile than systems based purely on deep learning techniques that need to be based upon particular examples and produce what can be considered variations of these examples. Our system allows the configuration of several key features to produce various genres and to adjust generated music according to user expectations. Table 4 shows data obtained with the group of experts during the training phase of the Critic, i.e., assignments for generated recordings belonging to three genres: relaxation, dance, and electronic, each containing 2000 audio files. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Genre** & **Correct (Class 1)** & **Incorrect (Class 2)** \\ \hline Relaxation & 940 & 1060 \\ Dance & 659 & 1341 \\ Electronic & 311 & 1689 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of correct and incorrect recordings for the three genres. Figure 11: Phrase (B) from Figure 10 with accompanying tracks—chords and bass. Figure 10: Three different phrases (**A–C**) produced by the Generator using motifs from Table 3. [X] marks a melody climax, [L] marks a longer ending note, and mot1-mot3 and dashed slurs mark the use of particular motifs. In order to determine the quality of the classification, the numbers of correctly classified positive and negative cases can be determined and marked, respectively, as TP (true positive) and TN (true negative) and incorrectly classified as FP (false positive) and FN (false negative). This is schematically presented in Table 5. We have seen significant differences in the classification results, relating to various genres of recordings. Out of 940 correct examples of relaxation music, 540 were correctly classified, accounting for about 57%. For dance music, 312 out of 659 correct examples were well classified, accounting for about 47%, and for electronic music, it was only 10% because 31 out of 311 correct recordings were appropriately classified. Referring to the incorrect class, we can see a better classification quality. For example, out of 1060 samples of relaxation music, 765 were properly classified, yielding about 72%. For dance and electronic music, it was about 89% and 99%, respectively. It needs to be pointed out that from a practical point of view, it is better to discard good music (classified as bad) and try to generate a new song again than to give a user a poorly generated piece as a correctly generated one. The accuracy is one of the quality factors of a classification, and it refers to the percentage of correctly classified samples. It is equal to (TP + TN)/(TN + FP + FN + TP). Presented models of the classifiers achieved an accuracy equaling 0.65 for relaxation music, 0.75 for dance music, and 0.85 for electronic music. Another well-known quality factor is the recall (sensitivity), which is the proportion of actual positives that are correctly classified. This indicator is depicted as TP/(TP + FN), and it was similar for relaxation, dance, and electronic music. It was equal to 0.65, 0.69, and 0.67, respectively. Unfortunately, for these generated samples, we were not able to obtain better classification results. As one can see, the problem may exist due to a small number of collections of the assessed recordings. Moreover, in the considered data, there were not enough good recordings to build an accurate classifier. For example, for electronic music, only 311 out of 2000 were rated as good recordings by experts.