## 2 Related Works
### Music Genre Classification Based on Audio Data  
Researchers achieved music classification by converting audio data into spectrograms and Mel Frequency Cepstral Coefficients (MFCC) images and subsequently applied texture analysis approaches for feature extraction. Various classifiers, such as K-nearest neighbors (KNN), Gaussian models, and support vector machines (SVMs), were utilized for classification. Notably, the KNN algorithm was effective in classifying classical music [19]. This approach was extended by introducing local binary patterns (LBPs) to extract textural features from spectrograms [20]. The extended version explored partitioning techniques to capture local information, emphasizing the importance of local features in enhancing the classification performance. Although these approaches were effective in specific scenarios, they were generally constrained by their focus on timbral features and failed to capture aspects of music that were potentially crucial for a more comprehensive understanding and music classification.  
Informative musical patterns could be automatically identified using CNNs [7]. Nonetheless, these rudimentary CNN models were restricted in generalizing previously unencountered music datasets. To overcome the limitations of these models in generalization and handling long-term temporal information, researchers proposed a hybrid model that combined residual neural networks (ResNets) and gated recurrent units (GRUs). This model used visual spectrograms as inputs and aimed to analyze music data more comprehensively. This approach [13] could improve the performance of music-recommendation systems through more accurate genre classification, thereby addressing the shortcomings of traditional machine learning and basic CNN models in handling the complexity of music data.  
By contrast, an approach has been proposed using S3T [16], which is a self-supervised pre-training approach with the Swin Transformer that effectively handles long-term information in music classification. This approach primarily aimed to learn meaningful music representations from a large corpus of unlabeled music data. It employed the momentum-based paradigm MoCo to serve as a feature extractor in the time-frequency domain of music and utilized a music data-augmentation pipeline and two specially designed preprocessors to further optimize the learning of music representations. However, this approach faced challenges such as an increased computational overhead owing to the management of large dynamic dictionaries, sensitivity to hyperparameter selection in the optimization process, and a lack of model interpretability.  
Progress in music classification had shifted from a focus on timbral features to more advanced and automated feature identification and extraction. Each stage of this evolution was associated with different challenges, ranging from limited generalization and a narrow focus on certain musical aspects to increased computational demands and complex training requirements. This underscored the ongoing need for obtaining more accurate and computationally efficient solutions and overcoming the persistent challenges of balancing model complexity and interpretability.