## 2 Related Works
### Deep Learning Method and Attention Mechanism for MER
It utilizes the CNN to learn emotion-related features through the filter bank and log Mel-spectrogram and further uses location-aware attention and self-attention [23] mechanisms to obtain salient emotion-related features. The authors in [27] proposed an end-to-end attention-based deep feature fusion (ADFF) approach for MER. The proposed model first uses an adapted VGGNet as a spatial feature learning module and then uses a squeeze-and-excitation (SE) attention-based [41] temporal feature learning module to obtain multi-level emotion-related spatial-temporal features. The experiments show that the combination of a spatial-temporal feature extractor and SE attention can achieve a better performance than the state-of-the-art model. In [28], a short-chunk CNN model with multi-head self-attention, called SCMA, and a BiLSTM model with multi-head self-attention, called BiLMA, are proposed for MER. It shows that the multi-head self-attention mechanism can effectively capture relevant information from features for emotion recognition tasks. Moreover, there are also some studies using a multimodal network to predict music emotion by combining various features such as symbolic, acoustic, and lyric features. In particular, a multimodal neural network for MER is proposed in [26], where audio features, lyric features, and context features are extracted separately and fused by a cross-modal attention mechanism. Similarly, a multimodal multifaceted MER method is proposed in [30], where symbolic and acoustic features are extracted from both MIDI and audio data and integrated with a self-attention mechanism. In [42], the authors propose an end-to-end one-dimensional residual temporal and channel attention network (RTCAN-1D) to fuse the subject's individual EDA features and the external evoked music features. The experiments show that the proposed method outperforms the existing state-of-the-art models. The representative works utilizing attention mechanisms are also summarized in Table 1.  
Overall, compared with traditional machine learning models, deep learning methods, along with their combination with attention mechanisms, can automatically extract more useful features for MER and lead to significant improvements in MER accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Reference** & **Year** & **Input Features** & **Learning Model** \\ \hline \multirow{8}{*}{\begin{tabular}{c} Machine \\ learning \\ \end{tabular} } & [13] & 2006 & Intensity, timbre, and rhythm & GMM \\  & [11] & 2010 & Rhythm patterns & KNN, SOM \\  & [34] & 2011 & Emotion vocabulary & NB, HMM, SVM \\  & [37] & 2014 & Acoustic feature, chroma feature & SVM \\  & [35] & 2018 & Slang presence, structural analysis & SVM \\  & [36] & 2018 & features, semantic features & SVM \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Deep learning \\ methods \\ \end{tabular} } & [20] & 2014 & A large set of acoustic features & Deep RNN \\  & [17] & 2016 & Low-level acoustic features & BiLSTM \\  & [15] & 2017 & Spectrogram & CNN \\  & [38] & 2019 & EEG & CNN \\  & [19] & 2020 & Spectrogram & CNN \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Deep learning + \\ attention \\ \end{tabular} } & [24] & 2017 & MFCCs, spectral flux, centroid, entropy, slope, etc. & LSTM + MCA \\  & [25] & 2021 & Log Mel-spectrogram and filter bank & CNN + local attention and global \\  & [27] & 2022 & Log Mel-spectrogram & VGGNet + SE Attention + BiLSTM \\  & [42] & 2022 & EDA, the external evoked & Residual channel-temporal attention \\  & [26] & 2022 & Mel-spectrogram, lyrics, track name, and artist & CNN + cross-modal attention \\  & [28] & 2023 & Mel-spectrogram/MIDI-like & Short-chunk CNN/BiLSTM + representation & multi-head self-attention \\  & [30] & 2023 & MIDI-like representation, & BiGRU + CNN + the multi-head \\  & \multicolumn{1}{c}{} & MFCCs, chromagram & self-attention \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative works of MER.