## 2 Related Works
### Deep Learning Method and Attention Mechanism for MER  
With the rapid development of deep learning, significant work has been conducted for MER by constructing various deep learning network architectures, and the accuracy has been greatly improved in recent years. The most widely used network is the convolutional neural network, which emulates biological visual perception and can effectively extract feature representations from music spectrogram data. For example, a deep CNN on music spectrograms is proposed for music emotion classification in [15]. By using the proposed method, no additional effort is required to extract specific features, which is left to the training procedure of the CNN model. [PERSON] et al. [38] used electroencephalogram (EEG) as the input feature to realize emotion recognition during music listening. CNNs with three to seven convolutional layers were employed in the research, and a binary classification task was measured. [PERSON] et al. [19] applied the constant-Q transform on music objects to derive the spectrogram and then took the spectrogram as the input of the CNN model to predict the dimensional emotion of music objects. In addition, the BiLSTM (bidirectional long short-term memory) model, as a two-way recurrent neural network with long short-term memory, is often used in emotion classification tasks due to its ability to process sequence data and maintain long-term memory. [PERSON] et al. [20] employed a deep RNN structure for online continuous-time music mood regression. The study first extracted a large set of segmental acoustic features and then performed multi-variate regression using deep recurrent neural networks. The results showed that the deep RNN outperformed SVR and feedforward neural networks in both continuous-time and static music mood regression. In [17], a deep bidirectional long short-term memory (DBLSTM)-based multi-scale regression method was proposed for dynamic music emotion prediction, in which a fusion component was introduced to integrate the outputs of all DBLSTM models with different scales. The experimental results show that the proposed method achieves a significant improvement when compared with state-of-the-art methods. The representative works using deep learning methods are also summarized in Table 1.  
More recently, the attention mechanism has also been combined with the deep learning methods, further achieving accuracy improvements for the MER tasks. In [24], the authors proposed multi-scale context-based attention (MCA) using LSTM for dynamic music emotion prediction. The proposed MCA mechanism pays different attention to the previous contexts of different time scales of music, and multi-scale models fused with attention can learn the deep representations of music structure dynamically and lead to better performance. In [39], a structure that combines 3D convolutions and attention-based sliding recurrent neural networks (ASRNNs) was proposed for speech emotion recognition, in which the 3D convolution model was proposed to obtain both the local features and periodicity information of emotional speech and the ASRNN was used to extract the continuous segment-level internal representations and focus on the salient emotion regions using a temporal attention model. The authors in [40] proposed two attention-based methods based on a VGG-ish architecture for the task of music emotion recognition. The first attention method used self-attention to replace the spatial convolutions in later layers of the VGG-ish network, and the second method used element-wise attention-based rectified linear units (ReLUs) in all the layers of the baseline VGG-ish network. The experimental results show that the first method can match the baseline performance with fewer computations and parameters, and the second method can achieve a better performance than the baseline without increasing the number of parameters. In [25], a novel attention-based joint feature extraction model was proposed for static MER.