# Technical, Musical, and Legal Aspects of an AI-Aided Algorithmic Music Production System
## 3 Results and Discussion
### Auditory Evaluation  
A listening test was carried out to evaluate the ability of the entire generator-critic system to produce music acceptable within boundaries of selected genres. A panel of listeners was given a task to evaluate if a given musical piece was a good example of a specific genre. A set of examples contained both fragments of musical recordings generated by our system and fragments of recordings produced by a human artist. Listeners were not informed about the origin of the evaluated recordings and were tasked with evaluating both the human- and computer-generated music under the same rules. The system generated 50 recordings in each of the three genres: relaxation music, dance music, and electronic music. Out of these, five recordings were randomly selected from each genre. Accordingly, five recordings for each of the same genres were randomly selected from a repository of tagged music produced by human artists. Thus, a set of 30 recordings was selected, 10 in each of the three genres. Out of each of the 10 recordings within a genre, 5 were created by human artists and 5 were automatically produced by our system. In order to limit the duration of the test to avoid an effect of fatigue on the part of the listeners, only a 30 s fragment from the middle of each recording was extracted, with 2 s of fade-in and fade-out applied. Recordings were normalized to \(-\)3 dB FS. Within \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Genre** & **Actual** & **Predicted Negative** & **Predicted Positive** \\ \hline Relaxation & Negative & TN = 765 & FP = 400 \\  & Positive & FN = 295 & TP = 540 \\ \hline Dance & Negative & TN = 1199 & FP = 347 \\  & Positive & FN = 142 & TP = 312 \\ \hline Electronic & Negative & TN = 1674 & FP = 280 \\  & Positive & FN = 15 & TP = 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of correct and incorrect predictions for three types of genres. each genre, a 10-element list of recordings was created, with 5 computer-generated and 5 human-created recordings randomly interleaved and arranged in random order. The listeners used headphones in a silent room. They could listen to the recordings in any order, without a limit on the number of replays. Each recording was evaluated for fitness for a given genre using a six-grade scale, from bad (1) to poor (2), fair (3), good (4), very good (5), and excellent (6), as recommended in the EBU (European Broadcasting Union) document [3]. The listeners were also asked to state their age group, music production or music distribution experience, and any hearing problems. Twenty listeners attended the test, 55% were between 20 and 29 years old, 36% were between 30 and 39 years old, and 9% were between 40 and 49 years old. In total, 55% had a professional experience with music production or distribution, 18% had an amateur experience, and 27% had minimal experience. No participants reported hearing loss. The aim of the test was to determine if the system can produce music acceptable within specific genres; therefore, we decided to evaluate the quality of the recordings using the following filters for answers:  
* accepted recordings, if human voted as fair, good, very good, or excellent,
* rejected recordings, if human voted as bad or poor. As represented in Figure 12, the dance music generated by our system had the lowest level of acceptance (about 24%). It is interesting to observe that the recordings generated as relaxation music were largely accepted by the listeners (about 78%). Rejections of human-created recordings may be unexpected, but they can be explained by the procedure of selecting recordings for the test. In order to compare generated content with typical real-world counterparts, human-created recordings were randomly picked from a large commercial repository based on tags provided by authors or publishers to reflect the way they are tagged in real-use cases.