## 4 Experiment
### Subjective Evaluation
It can be seen from the table that although the overall score of the music generated by the algorithm did not exceed that of the existing composers, the model improved the results in highlight and structure indexes, indicating that the music generated by the algorithm was more in line with the rhythm of the video than the existing music searched through a given scene using the matching function. A major factor affecting the overall score was emotion foil; the reason was that the music generated by the proposed algorithm did not take into account the content of the videos, and as a result, it had a great disadvantage in the video soundtracks with strong emotion, such as war field. At the same time, we analyzed the results from a single scenario. Figure 7 shows the average result from the expert evaluation group on the two kinds of music on different indicators, taking the night scene as an example and Figure 8 shows the average result from the group without music knowledge background. It can be concluded from the distribution in the following graphs that for the expert evaluation group, the structure score of the generated music was much larger than that of the composed music, because of the position encoder in the Transformer network. To our surprise, combined with changes of the video \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Rhythm** & **Emotion Foil** & **Highlight** & **Structure** & **Avg.** \\ \hline Night scene & 3.6 (3.3) & 2.8 (3.5) & 3.5 (3.7) & 4.0 (3.5) & 3.48 (**3.50**) \\ Desert animal & 3.1 (3.4) & 3.6 (3.1) & 3.1 (3.1) & 4.2 (3.6) & **3.50** (3.30) \\ War field & 2.8 (3.1) & 1.4 (2.8) & 3.5 (3.3) & 3.1 (2.9) & 2.70 (**3.03**) \\ Animal movie & 3.2 (3.7) & 2.4 (3.2) & 3.8 (3.3) & 3.8 (3.4) & 3.30 (**3.40**) \\ Character movie & 3.5 (3.6) & 3.3 (3.5) & 2.8 (3.2) & 4.0 (3.6) & 3.40 (**3.48**) \\ Avg. & 3.24 (**3.42**) & 2.70 (**3.22**) & **3.34** (3.32) & **3.82** (3.40) & 3.28 (**3.342**) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The score of the expert group on algorithmically generated music and music made by composers on 4 indicators. (The values in the table represent the score of the music generated by the algorithm, and the values in brackets represent the score of the music made by the composer.) Figure 6: The voting distribution of the two evaluation groups. motion speed, the generated music of the night scene was also slightly more rhythmic than the existing composed music selected using a matching algorithm. Different from the expert review panel, the listeners with no musical knowledge preferred the highlight of the generated music, which meant our model also improves the stress of the music by building a connection between local maximum motion saliency and note strength. However, the emotion foil score of the two groups was poorly experienced, which we need to consider in future works. Apart from the CAT method, we also compared the generated music with existing matching music of the composers by mixing the generated music and the music written by the composer into a set and labeled it. Then, we randomly selected the music for the two groups of participants and asked the user to distinguish whether it was generated by model or composed by composers. At the same time, participants were also asked to vote on whether they would have liked to use the heard music as background music  
Figure 8: The average result from the evaluation group without music knowledge background on two kind of music pieces on different indicators of night scene. Figure 7: The average result from the expert evaluation group on two kind of music pieces on different indicators of night scene. for the given video. Listeners judged the current music as \"It is generated by AI\", \"It is generated by Human\" or \"Unable to distinguish\".