# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 4 Results and Discussion
### Comparing Embedding and PCP Approaches  
Tables 5 and 6 show a comparison between the PCP Binary representation and the embeddings models (JukeMIR, Mule, and MERT). The best outcomes are highlighted in bold. These results clearly demonstrate the superiority of the Binary PCP representation in mode classification. While the Binary PCP achieves consistently high performance across all clustering algorithms, the embedding models struggle to effectively distinguish between the different modes. This limitation is evident in the clustering results, where a significant proportion of the data are assigned to the same cluster, suggesting that the embeddings do not capture the subtle tonal differences required for mode identification.  
The embedding-based approach introduces additional complexity by representing each tune within a high-dimensional feature space (e.g., 4800 dimensions for JukeMIR), which may obscure the fundamental characteristics that differentiate the modes. For instance, the embeddings tend to overfit certain acoustic patterns in the data, leading to reduced generalization across clusters. Conversely, the Binary PCP's simplified representation emphasizes the presence or absence of pitches, allowing the clustering algorithms to focus on the distinctive pitch-class distributions of each mode. This is particularly critical for distinguishing between modes that differ by only one or two notes, such as Ionian and Mixolydian or Dorian and Aeolian.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **JukeMIR** & **Mule** & **MERT** & **PCP Binary** \\ \hline \multirow{4}{*}{K-Means} & Dor & 0.4794 (C4) & 0.7371 (C1) & 0.3691 (C2) & **0.7542 (C2)** \\ \cline{2-5}  & Maj & 0.4791 (C4) & 0.7365 (C1) & 0.3921 (C2) & **0.9798 (C3)** \\ \cline{2-5}  & Min & 0.4935 (C4) & 0.6923 (C1) & 0.3893 (C2) & **0.8066 (C4)** \\ \cline{2-5}  & Mix & 0.4678 (C4) & 0.7414 (C1) & 0.4326 (C2) & **0.6600 (C1)** \\ \hline \multirow{4}{*}{Agglom.} & Dor & 0.4185 (C2) & 0.6541 (C3) & 0.4231 (C1) & **0.6919 (C1)** \\ \cline{2-5}  & Maj & 0.4097 (C2) & 0.6672 (C3) & 0.4120 (C1) & **0.9152 (C3)** \\ \cline{2-5}  & Min & 0.4207 (C2) & 0.6234 (C3) & 0.4032 (C1) & **0.7280 (C4)** \\ \cline{2-5}  & Mix & 0.4145 (C1) & 0.6740 (C3) & 0.4406 (C1) & **0.9033 (C2)** \\ \hline \multirow{4}{*}{DBSCAN} & Dor & 0.9229 (C1) & 1.0 (C1) & 0.9475 (C1) & 1.0 (C1) \\ \cline{2-5}  & Maj & 0.8970 (C1) & 1.0 (C1) & 0.9381 (C1) & 0.9997 (C1) \\ \cline{2-5}  & Min & 0.9199 (C1) & 1.0 (C1) & 0.8886 (C1) & 1.0 (C1) \\ \cline{2-5}  & Mix & 0.8830 (C1) & 1.0 (C1) & 0.9575 (C1) & 0.9956 (C1) \\ \hline \multirow{4}{*}{Mean Shift} & Dor & 0.9323 (C1) & 0.8392 (C1) & 0.9417 (C1) & 0.9948 (C1) \\ \cline{2-5}  & Maj & 0.9208 (C1) & 0.8364 (C1) & 0.9273 (C1) & 0.9992 (C1) \\ \cline{2-5}  & Min & 0.9325 (C1) & 0.8108 (C1) & 0.9435 (C1) & 0.9664 (C1) \\ \cline{2-5}  & Mix & 0.9220 (C1) & 0.8229 (C1) & 0.9326 (C1) & 0.9991 (C1) \\ \hline \multirow{4}{*}{SOM} & Dor & 1.0 (C4) & 1.0 (C3) & 1.0 (C4) & 0.99 (C2) \\ \cline{2-5}  & Maj & 1.0 (C4) & 1.0 (C3) & 1.0 (C4) & 0.99 (C2) \\ \cline{1-1} \cline{2-5}  & Min & 1.0 (C4) & 1.0 (C3) & 1.0 (C4) & 0.99 (C2) \\ \cline{1-1} \cline{2-5}  & Mix & 1.0 (C4) & 1.0 (C3) & 1.0 (C4) & 0.99 (C2) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of clustering purity for different embedding model representations and PCP Binary across modes. The number in parentheses indicates the dominant cluster for each mode.  
Overall, the results suggest that embedding models, while powerful in other music-related tasks, may not be well suited for mode classification in this specific context. Their inability to consistently detect the unique characteristics of diatonic modes underscores the advantages of using tailored, domain-specific features such as PCP Binary for unsupervised classification.