## 3 Music Emotion Regression Experiment
### Determination of Music Emotion Feature Sets  
In Section 3.1, the preliminarily optimal combination of pre-processing and feature selection was detailed. This section introduces the specific method to determine the music emotional feature set according to the combination. First, a data cleaning process was applied and 18 acoustic features with many missing values in the datasets were removed. Then, pre-processing was carried out using the MaxAbsScaler method. The feature selection stage is divided into two steps. In the first step, univariate analysis was carried out for each dataset. The filtering feature selection based on the correlation attribute evaluation method was used to remove 20% of the features that had the least influence on the regression results, reducing the number of features for the next step. In the second step, RFE was carried out using extremely randomized trees. In this algorithm, the prediction model was trained on the original features, and then the importance weight of each feature was obtained and sorted. The feature with the least weight was removed from the feature set, and this procedure was called recursively until only one feature was left. Then, a five-fold cross-validation method was adopted. The number of features that best ensured a high recognition rate was determined by calculating the regression R\({}^{2}\) score after each iteration. To select the number of features, two methods were adopted. One was the global optimal feature number, which is the number of features corresponding to the point where \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **StandardScaler** & **MinMaxScaler** & **MaxAbsScaler** & **QTU** & **QTG** \\ \hline \multirow{3}{*}{Western Music Valence} & Filter-C & 0.5743 & 0.5623 & 0.6289 & 0.6492 & 0.6725 \\  & Filter-I & 0.6520 & 0.6401 & 0.6698 & 0.6244 & 0.6693 \\  & Wrapper & 0.6442 & 0.6697 & 0.6804 & 0.6614 & 0.6232 \\  & Embedded & 0.6718 & 0.6798 & 0.6772 & 0.6497 & 0.6520 \\ \hline \multirow{3}{*}{Western Music Arousal} & Filter-C & 0.6164 & 0.5268 & 0.5277 & 0.5178 & 0.5917 \\  & Filter-I & 0.6140 & 0.6037 & 0.5326 & 0.5861 & 0.5822 \\  & Wrapper & 0.5377 & 0.6193 & 0.5967 & 0.6036 & 0.5797 \\  & Embedded & 0.5917 & 0.5659 & 0.5927 & 0.5931 & 0.5821 \\ \hline \multirow{3}{*}{Chinese Music Valence} & Filter-C & 0.5851 & 0.6055 & 0.6248 & 0.5964 & 0.5658 \\  & Filter-I & 0.5561 & 0.5895 & 0.6084 & 0.5161 & 0.6247 \\ \cline{1-1}  & Wrapper & 0.6101 & 0.6424 & 0.6707 & 0.6747 & 0.6189 \\ \cline{1-1}  & Embedded & 0.6628 & 0.5429 & 0.6306 & 0.6909 & 0.6737 \\ \hline \multirow{3}{*}{Chinese Music Arousal} & Filter-C & 0.6661 & 0.6280 & 0.6712 & 0.6155 & 0.5823 \\ \cline{1-1}  & Filter-I & 0.5746 & 0.4715 & 0.6027 & 0.6325 & 0.5890 \\ \cline{1-1}  & Wrapper & 0.6786 & 0.6654 & 0.6821 & 0.6589 & 0.6344 \\ \cline{1-1}  & Embedded & 0.6113 & 0.6222 & 0.6146 & 0.6787 & 0.6069 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: R\({}^{2}\) scores of different combinations of pre-processing and feature selection methods, the best combinations for the different datasets underlined. the maximum value of R\({}^{2}\) is found on the RFE curve, as shown in Figure 3. However, this could lead to a lot of redundant information, resulting in an excessive number of features. Consequently, the local optimal feature number was selected, which is the number of features corresponding to the first peak point on the curve envelope. In this algorithm, the minimum horizontal distance in feature numbers between neighboring peaks was 20. Smaller peaks were removed until the condition was fulfilled for all remaining peaks. In this way, a more appropriate number of features was obtained. Figure 3 shows the RFE curve of five-fold cross-validation, and the evaluation score is R\({}^{2}\). Table 3 displays the global optimal feature number, local optimal feature number, and their R\({}^{2}\) scores. As can be seen in Figure 3, the RFE curves of the four datasets were very flat, and all of them had very small variances.