# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 5 Conclusions  
In this study, our main objective was to determine the best preprocessing techniques in the task of mode detection in folk music, using Irish folk music as an example and focusing the work on unsupervised learning paradigm. To reach this objective, multiple experiments were performed to test different methods, such as different PCPs, embeddings, and reduction algorithms. In summary, the results highlight that the Binary PCP representation is the most effective feature for unsupervised mode classification of Irish folk music. Its \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **JukeMIR** & **Male** & **MERT** & **PCP Binary** \\ \hline \multirow{2}{*}{**K-Means**} & NMI & 0.0062 & 0.0005 & 0.0055 & **0.5807** \\ \cline{2-6}  & ARI & 0.0019 & 0.0091 & 0.0095 & **0.5626** \\ \hline \multirow{2}{*}{**Agglom.**} & NMI & 0.0061 & 0.0005 & 0.0091 & **0.5976** \\ \cline{2-6}  & ARI & 0.0004 & 0.0065 & 0.0025 & **0.5756** \\ \hline \multirow{2}{*}{**DBSCAN**} & NMI & 0.0 & 0.0 & 0.0 & 0.0 \\ \cline{2-6}  & ARI & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \multirow{2}{*}{**Mean Shift**} & NMI & 0.0 & 0.0 & 0.0 & 0.0 \\ \cline{2-6}  & ARI & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \multirow{2}{*}{**SOM**} & NMI & 0.0 & 0.0 & 0.0 & 0.5105 \\ \cline{2-6}  & ARI & 0.0 & 0.0 & 0.0 & 0.4065 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of clustering NMI and ARI values for different embedding model representations and PCP Binary across modes. simplicity and focus on pitch-class presence enable clustering algorithms to accurately identify the four diatonic modes, outperforming both the more complex embeddings and the other PCP variations. Among the clustering algorithms, K-Means and Agglomerative Clustering consistently deliver strong performance, achieving high accuracy in identifying clusters and correctly classifying modes. In contrast, DBSCAN and Mean Shift struggle due to their sensitivity to parameter selection and the lack of predefined clusters, leading to inconsistent results. Similarly, the embeddings, while offering high-dimensional representations, introduce unnecessary complexity and fail to capture the subtle tonal differences that distinguish the modes. These findings emphasize the importance of selecting appropriate feature representations and algorithms tailored to the specific task of diatonic mode classification. Finally, for future work, this study opens opportunities to explore unsupervised mode classification in other musical traditions and cultures, particularly those with modal systems distinct from Western diatonic modes. Extending the analysis to non-Western music could reveal how these techniques generalize to different modal frameworks and highlight potential adaptations required for culturally specific features. Additionally, comparing the performance of unsupervised learning and supervised approaches could provide deeper insights into the trade-offs between autonomy and accuracy. By incorporating labeled datasets, supervised methods could be used as a benchmark to evaluate the efficacy of unsupervised pipelines, offering a more comprehensive understanding of their strengths and limitations in mode classification tasks. Conceptualization, J.J.N.-C., D.M.J.-B. and M.N.-C.; methodology, J.J.N.-C., D.M.J.-B. and M.N.-C.; software, J.J.N.-C.; validation, J.J.N.-C., D.M.J.-B. and M.N.-C.; formal analysis, J.J.N.-C., D.M.J.-B. and M.N.-C.; investigation, J.J.N.-C.; resources, J.J.N.-C., D.M.J.-B. and M.N.-C.; data curation, J.J.N.-C.; writing--original draft preparation, J.J.N.-C.; writing--review and editing, D.M.J.-B. and M.N.-C.; visualization, J.J.N.-C.; supervision, D.M.J.-B. and M.N.-C.; project administration, M.N.-C.; funding acquisition, M.N.-C. All authors have read and agreed to the published version of the manuscript.