## 4 Results and Discussion
### Importance of Acoustic Features for Different Feature Sets  
The importance of acoustic features in the four music emotion feature sets was further analyzed based on the importance coefficients obtained through RFE feature selection using extremely randomized trees. The percentage of the importance of acoustic features for each feature set is displayed in Figure 4, in which the different colors of acoustic features represent different musical elements. For multi-dimensional acoustic features, the importance coefficients of multiple dimensions are summed to obtain the percentage of importance. In each feature set, the top five important features are shown in Table 6.  
For the Western classical music dataset, the number of important acoustic features is relatively small, and there are some features with a much higher percentage of importance, such as spectral contrast. For the Chinese classical music dataset, the number of important features is relatively large, and the percentage of feature importance is more evenly distributed. Some important acoustic features were analyzed and compared further to discover the emotional perception mechanism for Western and Chinese classical music datasets. \begin{table}
\begin{tabular}{c l} \hline \hline
**Data Subset** & \multicolumn{1}{c}{**Feature Sets**} \\ \hline \multirow{7}{*}{Western Classical Music—Valence} & HCDF\_mean, HPCP\_dmean\_28, Onset\_Rate, MFCC\_dmean\_1, MFCC\_dmean\_2, \\  & Spectral\_complexity\_dmean, Spectral\_flux\_mean Spectral\_complexity\_dvar, \\  & Spectral\_complexity\_mean, Spectral\_complexity\_var, Spectral\_entropy\_dmean, \\  & Spectral\_entropy\_mean, Spectral\_contrast\_coeffs\_dmean\_1, \\  & Spectral\_contrast\_coeffs\_dmean\_2, Spectral\_contrast\_coeffs\_dmean\_6, \\  & Spectral\_contrast\_coeffs\_dvar\_1, Spectral\_contrast\_coeffs\_dvar\_6, \\  & Spectral\_contrast\_coeffs\_mean\_6, Dissonance\_mean, \\  & Beats\_loudness\_band\_ratio\_mean\_5, Spectral\_centroid\_dmean \\ \hline \multirow{7}{*}{Western Classical Music—Arousal} & Tuning\_diatonic\_strength, Tuning\_equal\_tempered\_deviation, \\  & Spectral\_contrast\_coeffs\_mean\_2, Spectral\_contrast\_coeffs\_mean\_3, \\  & Spectral\_contrast\_coeffs\_dmean\_2, Spectral\_contrast\_coeffs\_dmean\_6, \\  & Spectral\_contrast\_coeffs\_dvar\_6, Spectral\_skewness\_dvar, Spectral\_complexity\_var, \\  & HPCP\_entropy\_dmean, HPCP\_d\_mean\_12, HPCP\_dvar\_12, HPCP\_entropy\_mean, \\  & HCDF\_Mean, HCDF\_Period\_Amp, MFCC\_var\_5, MFCC\_var\_6 \\ \hline \multirow{7}{*}{Chinese Classical Music—Valence} & Onset\_rate, HCDF\_mean, Spectral\_strongpeak\_dmean, Spectral\_complexity\_dmean, \\  & HFC\_dmean, Spectral\_flux\_mean, Spectral\_flux\_dmean, \\  & Spectral\_contrast\_coeffs\_mean\_2, Spectral\_contrast\_coeffs\_dmean\_4, \\  & Spectral\_strongpeak\_dvar, Spectral\_strongpeak\_var, Beats\_loudness\_band\_ratio\_mean\_3, \\  & BPM\_histogram\_first\_peak\_weight\_mean, Silence\_rate\_60 dB\_var, Dynamic\_complexity, \\  & MFCC\_dmean\_2, MFCC\_mean\_6, MFCC\_dmean\_9, MFCC\_dvar\_13, Dissonance\_mean \\ \hline \multirow{7}{*}{Chinese Classical Music—Arousal} & Spectral\_flux\_mean, Spectral\_complexity\_dmean, Dissonance\_mean, Dissonance\_var, \\  & HCDF\_PeriodAmp, Spectral\_entropy\_mean, AHFC\_dmean, Spectral\_skewness\_mean, \\ \cline{1-1}  & Spectral\_strongpeak\_dmean, Spectral\_contrast\_coeffs\_mean\_2, Onset\_rate, HCDF\_mean, \\ \cline{1-1}  & ERBbands\_mean\_30, ERBbands\_spread\_dmean, Silence\_rate\_60 dB\_var, \\ \cline{1-1}  & HPCP\_entropy\_mean, HPCP\_dvar\_1, HPCP\_dmean\_1, MFCC\_dmean\_2, MFCC\_mean\_1, \\ \cline{1-1}  & MFCC\_dmean\_7, MFCC\_dmean\_8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Music emotional feature sets of Western and Chinese classical music. As shown in Figure 5, the spectral contrast is the difference in strength between the spectral peaks and valleys in each spectral subband divided by octave-scale filters.