## 8 Experiment & Results
### Qualitative Studies  
Figure 13 shows the traditional Chinese folk pentatonic music randomly generated by 3D-SCN and BoYaTCN models, in which the traditional Chinese pentatonic scale \(Gong\) (red), \(Shang\) (orange), \(Jue\) (yellow), \(Zhi\) (blue), and \(Yu\) (purple) notes account for about 80%, and the rest are mostly the other four partials of the traditional Chinese folk pentatonic scale: \(Qingjiao\), \(Bianzhi\), \(Biangong\), and \(Run\). Among them, it is not difficult to find that the generated traditional pentatonic folk music is characterized by timbral variations. Such a result may be the effect of the translation invariant architecture training of the convolutional network used in 3D-SCN, and the whole story is harmonious. We can call it a transposition \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Loss** & **Precision** & **Recall** \\ \hline TP-LSTM-NADE & 99.01 & 0.782 & 0.452 & 0.236 \\
3D-SCN & 99.04 & 0.775 & 0.422 & 0.230 \\ DP-TCN & 88.72 & 0.845 & 0.332 & 0.381 \\ MH-TCN & 89.23 & 0.824 & 0.332 & 0.371 \\ BoYa+TCN & 99.08 & 0.724 & 0.473 & 0.183 \\
**BoYaTCN** & **99.12** & **0.708** & **0.499** & **0.412** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of objective machine learning metrics for ablation experiments based on three-dimensional sequential convolutional networks. The pitch-duration-frequency statistics graphs of traditional Chinese folk music fragments generated by 3D-SCN and BoYaTCN sequence convolution architectures also illustrate some things. The generated note duration of quarter notes, eighth notes, and sixteenth notes are observed. Looking at the pitch distribution from traditional Chinese pentatonic folk music, they all also appear as _Gong, Shang, Jue, Zhi, Yu, Qingjiao, Bianzhi, Biangong_, and _Run_, with the scale composition of traditional Chinese folk music of Qingyue, Yanyue, and Yayue, as shown in Figure 14.  
As the style characteristics of Chinese traditional folk music, the interval compositions of the major second and minor third also provide strong support for the experimental generation of the interval composition characteristics of Chinese traditional pentatonic folk music. The note probability transfer matrix of the generated music fragments from 3D-SCN and BoYaTCN shows that the generated music fragments have a major second and minor third as the main interval composition, as shown in Figure 15.  
The musical melodies generated by the 3D-SCN and BoYaTCN music generation models were also relatively smooth and stable with even energy distribution. The intervals span across three and a half octaves and range from one-lined octave to three-lined octave, as shown in Figure 16.  
The piano roll visualization demonstrated the duration and pitch information of the notes and the distribution composition of the musical phrases, with the vertical axis representing the pitch and the horizontal axis representing the time span of the event. It can be observed that the note-density is relatively reasonable, and the phrases are smoothly articulated and well-structured as the period changes. The phrase bars 4-10 and 10-16 also produce the transpositional quality of the music composition, reflecting the success of the panning invariance of the network architecture design. The structure between measures 4-10 and 10-16 also generates a circular and reciprocal form of musical composition. However, the fragmented notes are of short duration and there are sharp pauses between notes, but the transposition is more evident in phrase bars 19-25 and 25-31. Compared to 3D-SCN, BoYaTCN achieves better results in the structural learning of the music at the phrase level, as shown in Figure 17.  
Subjective user research was also conducted in this work. A total of 403 subjects were recruited using Web-based questionnaires requesting professors and students from university and evaluate traditional Chinese pentatonic folk music randomly generated by the proposed model.