## 9 Conclusions  
In this paper, we presented and studied a music generation method based on LSTM, CNN, and musical attention to improve the content generation performance of Chinese folk music. This resulted in a multi-generational selection, and we presented two successful models to implement this idea: three-dimensional sequential convolutional network (3D-SCN) and bidirectional octave your attention temporal convolutional network (BoYaTCN). The main ideas include modeling the note data based on the 3D spatial perspective of BiLSTM in an attempt to learn the temporal embedding of musical events in the context of integrated musical fragments. It also incorporates a multi-layer attention mechanism for designing convolutional modules for music data embedding, both of which capture the relevant musical features for music specificity. We have tested the proposed ablation experiments with multiple parameters on a traditional Chinese folk music dataset. Objective and subjective studies demonstrate that the proposed model is capable of producing pentatonic music that is more melodic and harmonically coherent, and has distinctive traditional Chinese pentatonic tonal characteristics. It also conforms to certain musical syntactic features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **User Type** & **Overall Style** & **Harmonious** & **Rhythmic** & **Structured** & **Nationality** \\ \hline \multirow{3}{*}{TP-LSTM-NADE} & Pro & 89.86 & 93.32 & 79.57 & 87.23 & 85.45 \\  & Non-pro & 82.24 & 90.41 & 75.63 & 83.61 & 82.25 \\  & All & 84.42 & 90.78 & 76.82 & 84.68 & 83.57 \\ \hline \multirow{3}{*}{**3D-SCN**} & Pro & 90.70 & 96.51 & **81.40** & 88.37 & 89.53 \\  & Non-pro & 83.60 & **95.59** & **84.54** & 89.28 & 86.12 \\  & All & 85.11 & **95.78** & **83.87** & 89.09 & 86.85 \\ \hline \multirow{3}{*}{DP-TCN} & Pro & 77.98 & 82.56 & 70.95 & 78.37 & 76.41 \\  & Non-pro & 73.32 & 78.72 & 68.03 & 73.48 & 72.26 \\  & All & 75.46 & 80.17 & 68.49 & 75.62 & 74.57 \\ \hline \multirow{3}{*}{MH-TCN} & Pro & 79.26 & 83.24 & 73.74 & 80.45 & 78.38 \\  & Non-pro & 74.84 & 79.33 & 70.34 & 75.89 & 75.56 \\  & All & 76.21 & 80.72 & 71.98 & 77.95 & 76.24 \\ \hline \multirow{3}{*}{BoYa+TCN} & Pro & 80.15 & 85.22 & 75.41 & 82.68 & 82.12 \\  & Non-pro & 72.74 & 82.04 & 71.62 & 79.57 & 80.46 \\  & All & 78.71 & 83.48 & 72.71 & 80.06 & 80.98 \\ \hline \multirow{3}{*}{**BoYaTCN**} & Pro & **91.21** & **96.67** & 82.61 & **92.41** & **90.15** \\  & Non-pro & **84.23** & 94.78 & 85.17 & **90.15** & **89.38** \\ \cline{1-1}  & All & **86.26** & 95.29 & 84.03 & **91.34** & **89.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of randomly generated music by music professionals and non-professionals. Figure 17: Fragment of a piano roll of a random pentatonic scale music sample from 3D-SCN (**a**) and BoYaTCN (**b**). In the future, we plan to expand this research and provide multimode (audio signals, musical notation) musical compositions describing the same semantic content to explore more aesthetically and culturally valuable music generation content. Furthermore, the proposed generative model, with the support of diverse music theories, can also be safely transferred to generative tasks in other music genres, which is another work we will involve in the future. Conceptualization, F.J.; methodology, F.J.; software, F.J.; validation, K.W., X.D., and W.Y. ; formal analysis, F.J. and K.W. ; investigation, L.Z. ; resources, L.Z. ; data curation, L.Z. and F.J.; writing--original draft preparation, F.J.; writing--review and editing, L.Z. and W.Y. ; visualization, F.J.; supervision, L.Z. ; project administration, L.Z. ; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Shaanxi Key Laboratory for Network Computing and Security Technology grant number NCST2021 YB-04, the scholarship from National Natural Science Foundation of China grant number 61802301 and 211817019, Shaanxi Natural Science Foundation of China grant number 2019 JQ-056.