## 5 Conclusions  
A pre-trained model, Deformer, was introduced to address the specific challenges associated with existing Swin transformer-based approaches in music genre classification within the context of MIR. These challenges include the computational burden associated with managing large dynamic dictionaries, the finicky nature of the contrastive loss function with respect to hyperparameter choices, and the low level of model interpretability commonly observed in MoCo-based approaches. Utilizing a two-stage process of pre-training and fine-tuning, the proposed model leveraged unlabeled audio data during the pre-training stage. The experimental results underscore the significance of incorporating Deformer in the realm of deep learning architectures for audio music classification. The proposed method achieved an accuracy of 84%, outperforming the ResNet-BiGRU-based (81%) and S3T-based (81.1%) models. This highlights the substantial contribution of Deformer to superior performance in audio classification, marking a noteworthy advancement over traditional approaches.  
Regarding its limitations, the proposed model was not assessed on larger or more diverse datasets, creating gaps in information regarding its generalizability. Future research directions could involve restructuring the architecture of the model to enable it to better handle genres that have subtle similarities, such as \"rock\" and \"blues\". The focus should be on enhancing the ability of the model to distinguish between closely aligned genres. Further improvements can be made to evaluate the performance of the model across a more diverse set of music genres and use cases. By pursuing these avenues, this research would not only add to the growing literature in the domain of music genre classification but also set a strong performance standard in subsequent investigations.  
Figure 12: Confusion matrix of audio music genre classification performed by Deformer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline ResNet-BiGRU & 0.81 & 0.82 & 0.81 & 0.81 \\ S3T & 0.811 & - & - & - \\ M2D & 0.839 & - & - & - \\ Juckbox & 0.797 & - & - & - \\ Deformer (w/o pre-train) & 0.37 & 0.3334 & 0.37 & 0.3464 \\ Deformer (pre-trained) & 0.845 & 0.844 & 0.845 & 0.844 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison: audio music classification models.  
**Author Contributions:** Conceptualization, J.W., S.L. and Y.S.; methodology, J.W., S.L. and Y.S.; software, J.W. and S.L.; validation, J.W., S.L., and Y.S.; formal analysis, J.W., S.L. and Y.S.; data curation, J.W. and S.L.; writing--original draft preparation, J.W.; writing--review and editing, J.W., S.L. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021R1F1A1063466). This work was supported by the Dongguk University Research Fund of 2023.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The data can be found here [[https://magenta.tensorflow.org/datasets/maestro](https://magenta.tensorflow.org/datasets/maestro)]([https://magenta.tensorflow.org/datasets/maestro](https://magenta.tensorflow.org/datasets/maestro)) and [[http://marsysa.info/download/data_sets](http://marsysa.info/download/data_sets)]([http://marsysa.info/download/data_sets](http://marsysa.info/download/data_sets)).  
**Conflicts of Interest:** The authors declare no conflict of interest.