## 5 Conclusions  
A comparative study of emotional feature sets between Western and Chinese classical music was conducted using emotion regression prediction. To compare the combination algorithms of pre-processing and feature selection methods, the optimal combination algorithm was determined. Twenty-dimension feature sets for valence and arousal of Western and Chinese classical music datasets were extracted from over 500-dimension acoustic features based on emotion regression prediction. The importance of representative acoustic features and musical elements of different feature sets was analyzed and compared, and the following conclusions were obtained:  
* Based on our cross-cultural dataset, the optimal combination algorithm of pre-processing and feature selection is MaxAbsScaler pre-processing and the wrapper method using RFE based on extremely randomized trees. * The number of important acoustic features for Western classical music dataset is larger than that for Chinese classical music dataset. For the Western classical music dataset, the distribution of the importance of acoustic features is mainly concentrated  
Figure 7: The distribution of the average importance of musical elements in four feature sets: (**a**) comparing the results of valence between Western and Chinese classical music, (**b**) comparing the results of arousal between Western and Chinese classical music, (**c**) comparing the results of valence and arousal based on the cross-cultural dataset, and (**d**) comparing the results of Western and Chinese classical music. on several features such as spectral contrast, whereas for the Chinese classical music dataset, the difference in the importance of each feature is relatively small. * Spectral contrast is the most significant feature for both valence and arousal perception of the Western classical music dataset. HCDF is significant for both valence and arousal perception of the Western and Chinese classical music datasets, which indicates that HCDF is culturally universal. Regardless of whether it is a Western or Chinese classical music dataset, the onset rate's influence on valence perception is stronger than its influence on arousal. Compared with Western classical music dataset, spectral flux is more important for valence and arousal perception of the Chinese classical music dataset. Dissonance is a culturally specific feature of the Chinese classical music dataset. * For valence, although pitch and rhythm are very important for both cultures' classical music dataset, loudness is specifically significant for Chinese classical music dataset. For arousal, pitch and timbre are the most important musical elements for the Western classical music dataset, whereas all musical elements have a significant impact on the Chinese classical music dataset, especially rhythm features. These research conclusions may provide some inspiration in the field of MIR, MER, music creation, and artificial intelligence composition. In particular, for cross-cultural music research, this paper can provide the data basis and acoustical perspective for further discussion. Future expansions of this study may include evaluating more datasets and applying a more detailed emotional model for evaluation to accumulate more experience in exploring cross-cultural music emotion perception. Conceptualization, X.W. ; Data curation, L.W. and L.X. ; Project administration, X.W. and L.X. ; Supervision, L.X. ; Writing--original draft, L.W. ; Writing--review & editing, X.W. and L.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Humanity and Social Science Youth Foundation of the Ministry of Education of China (15 YJCZH178) and the Science Foundation of the Communication University of China (HG1608-1) to [PERSON] and Major Project of Beijing Social Science Foundation (20 ZD20) to [PERSON]. The study was approved by the Research Ethics Committee of Communication University of China.