## 2 The Datasets and Acoustic Features  
### The Datasets  
Dataset quality depends on three factors: the number of excerpts in the dataset, the number of ratings per excerpt, and the method by which the data are annotated. We focus on these three factors to guarantee the validity of our datasets.  
* Western Classical Music Dataset  
The emoMusic [27] and Soundtracks [28] datasets that focus on music emotional analysis were selected as Western classical music datasets. The emoMusic dataset consists of 1000 representative songs selected from the Free Music Archive (FMA), with a 45 s clip excerpted from each song. The V-A model was adopted as the emotional model. More than 350 participants (72% from the USA) on Amazon Mechanical Turk were recruited to participate in this annotation after passing a qualification test. Participants were asked to score the perceived emotion rather than evoking one. Each participant annotated 3 songs on arousal and valence dimensions on a continuous scale ranging from 1 to 9, and each excerpt was annotated by a minimum of 10 participants. The average scores of each excerpt across participants were taken as the ground truth data. Krippendorff's alpha was calculated to measure the inter-annotation agreement, which indicated \"fair\" agreement (0.32 for valence and 0.35 for arousal). From this dataset, 78 excerpts of Western classical music were chosen for our study, including solo and ensemble music.  
The Soundtracks dataset contains 360 musical excerpts from a large selection of film soundtracks, with individual excerpts being between 10 and 30 s long. Twelve Western musicians who had studied Western musical instruments for 10 years or more were recruited to choose excerpts of different target emotions, such as happy, angry, sad, and calm. No excerpt was to contain lyrics, dialogue, or sound effects, and each was to be a Western music ensemble or instrumental solo. A three-dimensional emotion model including valence, tension arousal, and energy arousal was adopted [29], and the same group of musicians took part in this annotation to rate the three dimensions on a continuous scale of 1-7. [PERSON]'s alpha was calculated to measure the inter-annotation agreement, which indicated that all scales had good internal consistency (0.92 for valence, 0.90 for energy arousal, and 0.93 for tension arousal). The average scores across all participants were taken as the ground truth. Because [PERSON]'s correlation between valence and tension arousal was \(-\)0.91, the data of valence and energy arousal were selected to represent the ground truth of the V-A model. From this dataset, 360 musical excerpts were selected for our study. Overall, the total number of excerpts in our Western classical music dataset was 438.
* Chinese Classical Music Dataset  
We created the Chinese classical music dataset for this study, consisting of 500 musical excerpts from famous Chinese classical music albums, national instrumental music compilation discs, and online collected folk music albums, including instrumental solo and ensemble music of bowed chordophones, plucked chordophones, aerophones, and membranous. The length of each excerpt was approximately 30 s with stable emotion and complete phrases. The stimuli were stored on a laptop computer with Windows 10 and played back through Audio-technica CKM77 headphones. All stimuli were calibrated for loudness by volunteers. The monitor level was adjusted to a comfortable level by the participants. A total of 20 participants with Chinese backgrounds majoring in audio technology annotated the valence and arousal emotions on a continuous scale of 1-9. [PERSON]'s alpha indicated good inter-annotation agreement (0.95 for valence and 0.93 for arousal). The average values across all participants were used as the ground truth.  
In summary, the total number of musical excerpts in our cross-cultural dataset was 938.