## 1 Introduction
As a result of the progress in music globalization, an increasing number of researchers have begun to focus on the influence of different cultures on the emotional perception of music. Studies have shown that in addition to the characteristics of music, the cultural environment affects people's perception of music emotions [20], and people from different cultural backgrounds may have significant differences in the way they perceive emotions in the same music [21]. In cross-cultural dataset music emotion recognition (MER), most studies employ the MEC model [22; 23; 24]. Studies based on emotion regression models, especially for Western and Chinese classical music, are still limited. A study by [PERSON] et al. represents one of the first attempts to adopt a regression approach for MER based on Western, Chinese, and Japanese pop music datasets [25]. However, their best results for the emotional regression model were 58.3% for arousal and 28.1% for valence. Similarly, [PERSON] and [PERSON] explored the generalizability of emotion regression models for Western and Chinese pop music [26]. It was found that loudness and timbre features work well for both valence and arousal prediction. The size of the training datasets and the annotation reliability level of training and testing datasets can affect the regression performances on both valence and arousal, especially for cross-cultural and cross-dataset music emotional prediction. Although [PERSON] and [PERSON] evaluated three distinct cross-cultural datasets, all of them were composed of pop music. Because Western pop music is now ubiquitous in China, the standard instrument arrangement of pop music is relatively unified. There were no significant differences between Western and Chinese pop music in terms of melody, harmony, and orchestration, which may make it difficult to accurately extract culturally representative feature sets. Therefore, for our study we focused on more culturally representative Western and Chinese classical music as our research object in order to explore the influence of culture on MER. Furthermore, we also considered different kinds of algorithmsat each step of the training process to maximize the performance of a MER system based on the regression approach, from pre-processing to feature selection and classification model selection. Our study aimed to extend the previous research and focused on classical music to investigate the following three research questions:  
1. For Western and Chinese classical MER datasets, what kind of combination algorithm of pre-processing and feature selection methods can achieve the optimal effect of emotion regression prediction? 2. For Western and Chinese classical music, which acoustic features should be selected as being the most culturally representative and effective for participants' MER, respectively? 3. Based on extracted feature sets for different music datasets, what are the differences in the influences of different music elements on emotion regression prediction for different datasets? Figure 1 shows the workflow of emotional feature set analysis using regression prediction designed to investigate the research questions. The findings from our study try to establish a connection between musical elements and emotion perception in a cross-cultural context, which is useful for musicians to understand and apply when composing different emotional music. The remainder of this paper is organized as follows. Section 2 introduces the datasets used in this study and the method for extracting acoustic features. Section 3 describes the process of MER predictions for Western and Chinese classical music, along with feature sets for different datasets, which are obtained based on the optimal combination of pre-processing and feature selection methods. The experimental results and detailed discussions are presented in Section 4. Then, we draw conclusions by summarizing the findings of this study in Section 5. Figure 1: Workflow of emotional feature sets analysis using regression prediction.