# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 4 Experiments
### Results  
Table 4 shows the comparison between our model and other baseline models on the phrase segment task.  
As shown clearly in the table, the model proposed in this study outperforms the other four baseline models across all three evaluation metrics: precision, recall, and F1-score, demonstrating a significant advantage. Notably, the F1-score reaches 83%, representing a 5-percentage-point improvement over the previous best-performing model (the ensemble temporal prediction model), which achieved 78%.  
Specifically, our model achieves 83% precision and 84% recall, striking an effective balance between the two. In contrast, other models tend to favor one metric over the other: for instance, rule-based models prioritize recall (81%) but suffer from low precision (64%), while models such as random forest achieve high precision (83%) but much lower recall (69%). This indicates that the model developed in this study is more capable of balancing precision and recall, thereby significantly improving the overall performance of the phrase segmentation task.  
In summary, the proposed model demonstrates stronger discriminative ability and robustness in musical phrase segmentation, accurately capturing phrase boundaries information. This provides a more reliable foundation for subsequent music analysis and understanding tasks. These results further validate the feasibility and effectiveness of the proposed method in phrase segmentation and highlight its strong potential for generalization and practical application.  
To further analyze the model's ability to handle different musical structures, we investigated its performance on common meter types (2, 3, 4, and 6 beats per measure). The results are shown in Table 5, which demonstrate consistent improvement in F1-score across all meter types compared to ensemble temporal prediction, highlighting the model's  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline Rule Based [45] & 64 & 81 & 68 \\ Ripper [46] & 78 & 63 & 69 \\ Random Forest [46] & 83 & 69 & 76 \\ Ensemble Temporal Prediction [47] & 77 & 81 & 78 \\ Ours & 83 & 84 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our model to other baseline models on the Essen Folksong Dataset.  
superior generalization ability. Notably, the model exhibits strong performance in meters 2, 3, and 4, achieving F1-scores of 83.38, 83.03, and 84.24, respectively.  
These findings show that the model's ability to accurately segment phrases across diverse meter types opens new avenues for tasks in MIR. By identifying the boundaries of musical phrases, the model provides valuable insights into the underlying structure and organization of music, paving the way for more sophisticated music analysis and understanding. Furthermore, the model demonstrates a more stable performance across different meter types compared to other methods, suggesting its robustness in handling diverse musical structures. It is particularly noteworthy that our model performs significantly better on triple meters, which are often considered more challenging for phrase segmentation algorithms.