## 1 Introduction  
With the development and popularization of the Internet, online music applications have gradually become the main channel for people to listen to music. Now, people can listen to all kinds of music from various countries on the Internet. One of the challenges for online music services is to automatically classify large amount of music that meets people's listening needs. Emotion-based music organization and retrieval technology are feasible, and the core is music emotion recognition (MER). The main work is to construct an emotion calculation model based on audio content and text information to realize the process of automatic recognition of music emotion [1, 2]. The three research directions in MER are music emotion classification (MEC), emotion regression prediction, and music emotion variation detection, with acoustic feature extraction being one of the most important steps [3]. This addresses the analysis and extraction of meaningful information from audio signals to obtain a compact and expressive description that is machine processable [4]; this significantly contributes to the performance of the classification or regression system, which is always the focus of MER research. In this study, we adopted the valence-arousal emotional model (hereinafter termed V-A model) instead of a categorical model, treating MER as a continuous regression problem [5]. The V-A model, proposed by [PERSON], believes that the emotional state is a point distributed in a two-dimensional space containing valence and arousal [6]. The valence reflects the degree of positive and negative emotions, and the degree of arousal reflects the intensity of emotions [7]. Two reasons for employing the V-A model are (1) to avoid ambiguities with the terms used to label emotions and (2) to generate playlists with smooth transitions from one emotion to another. There have been many studies on the acoustic feature set of MER focusing on Western music [8; 9; 10; 11]. [PERSON] et al., proposed an algorithm called multi-emotion similarity preserving embedding and found that the mean and standard deviation of spectral flux, the first component of mel-frequency cepstral coefficients (MFCC), and the first and second beat histogram peaks in BPM are the most important features for MEC [12]. [PERSON] et al., summarized the methods of MER based on data features. They mentioned that the acoustic features usually used in MER include MFCC, octave-based spectral contrast, statistical spectrum descriptors (spectral centroid, flux, roll-off, and flatness), and Chromagram [2]. [PERSON] et al., applied a shrinkage method to feature selection in the arousal emotional dimension and proposed that low specific loudness sensation coefficients, root mean square, and loudness-flux are the most useful features [13]. [PERSON] examined the influence of different feature sets on valence and arousal prediction and concluded that a combination of different types of features can improve the results [14]. Rhythm features are important for arousal prediction, and tonal features are useful for detecting valence. A large quantity of research and applications of MER also exist in the related fields of neuroscience. [PERSON] described a theory of the neurobiological foundations of aesthetics and art, which have roots in emotion [15]. The latest research explored the high association between emotional arousal and neuro-functional brain connectivity measures [16]. Furthermore, as an important application of neuroscience, deep learning has recently become a popular topic in MER studies. Many paradigms have been proposed such as Convolutional Neural Networks (CNNs) applied to spectrograms or MFCC trajectories, Long Short-Term Memory layers (LSTM) for modeling of longer temporal context in music, and other deep learning strategies [17; 18; 19]. Although these end-to-end models have gradually become the mainstream algorithm, they do not rely on handcrafted features and are incapable of computing feature importance.